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Merge pull request #82 from VRandme/eca
ECA-Net Efficient Channel Attention
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*.tar | ||
*.pth | ||
*.gz | ||
Untitled.ipynb | ||
Testing notebook.ipynb |
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''' | ||
ECA module from ECAnet | ||
original paper: ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks | ||
https://arxiv.org/abs/1910.03151 | ||
https://github.com/BangguWu/ECANet | ||
original ECA model borrowed from original github | ||
modified circular ECA implementation and | ||
adoptation for use in pytorch image models package | ||
by Chris Ha https://github.com/VRandme | ||
MIT License | ||
Copyright (c) 2019 BangguWu, Qilong Wang | ||
Permission is hereby granted, free of charge, to any person obtaining a copy | ||
of this software and associated documentation files (the "Software"), to deal | ||
in the Software without restriction, including without limitation the rights | ||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
copies of the Software, and to permit persons to whom the Software is | ||
furnished to do so, subject to the following conditions: | ||
The above copyright notice and this permission notice shall be included in all | ||
copies or substantial portions of the Software. | ||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
SOFTWARE. | ||
''' | ||
import math | ||
from torch import nn | ||
import torch.nn.functional as F | ||
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class EcaModule(nn.Module): | ||
"""Constructs a ECA module. | ||
Args: | ||
channel: Number of channels of the input feature map for use in adaptive kernel sizes | ||
for actual calculations according to channel. | ||
gamma, beta: when channel is given parameters of mapping function | ||
refer to original paper https://arxiv.org/pdf/1910.03151.pdf | ||
(default=None. if channel size not given, use k_size given for kernel size.) | ||
k_size: Adaptive selection of kernel size (default=3) | ||
""" | ||
def __init__(self, channel=None, k_size=3, gamma=2, beta=1): | ||
super(EcaModule, self).__init__() | ||
assert k_size % 2 == 1 | ||
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if channel is not None: | ||
t = int(abs(math.log(channel, 2)+beta) / gamma) | ||
k_size = t if t % 2 else t + 1 | ||
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self.avg_pool = nn.AdaptiveAvgPool2d(1) | ||
self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False) | ||
self.sigmoid = nn.Sigmoid() | ||
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def forward(self, x): | ||
# feature descriptor on the global spatial information | ||
y = self.avg_pool(x) | ||
# reshape for convolution | ||
y = y.view(x.shape[0], 1, -1) | ||
# Two different branches of ECA module | ||
y = self.conv(y) | ||
# Multi-scale information fusion | ||
y = self.sigmoid(y.view(x.shape[0], -1, 1, 1)) | ||
return x * y.expand_as(x) | ||
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class CecaModule(nn.Module): | ||
"""Constructs a circular ECA module. | ||
the primary difference is that the conv uses a circular padding rather than zero padding. | ||
This is because unlike images, the channels themselves do not have inherent ordering nor | ||
locality. Although this module in essence, applies such an assumption, it is unnecessary | ||
to limit the channels on either "edge" from being circularly adapted to each other. | ||
This will fundamentally increase connectivity and possibly increase performance metrics | ||
(accuracy, robustness), without signficantly impacting resource metrics | ||
(parameter size, throughput,latency, etc) | ||
Args: | ||
channel: Number of channels of the input feature map for use in adaptive kernel sizes | ||
for actual calculations according to channel. | ||
gamma, beta: when channel is given parameters of mapping function | ||
refer to original paper https://arxiv.org/pdf/1910.03151.pdf | ||
(default=None. if channel size not given, use k_size given for kernel size.) | ||
k_size: Adaptive selection of kernel size (default=3) | ||
""" | ||
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def __init__(self, channel=None, k_size=3, gamma=2, beta=1): | ||
super(CecaModule, self).__init__() | ||
assert k_size % 2 == 1 | ||
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if channel is not None: | ||
t = int(abs(math.log(channel, 2)+beta) / gamma) | ||
k_size = t if t % 2 else t + 1 | ||
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self.avg_pool = nn.AdaptiveAvgPool2d(1) | ||
#pytorch circular padding mode is bugged as of pytorch 1.4 | ||
#see https://github.com/pytorch/pytorch/pull/17240 | ||
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#implement manual circular padding | ||
self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=0, bias=False) | ||
self.padding = (k_size - 1) // 2 | ||
self.sigmoid = nn.Sigmoid() | ||
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def forward(self, x): | ||
# feature descriptor on the global spatial information | ||
y = self.avg_pool(x) | ||
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#manually implement circular padding, F.pad does not seemed to be bugged | ||
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 | ||
y = self.conv(y) | ||
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# Multi-scale information fusion | ||
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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