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Update EcaModule.py

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Make pylint happy
(commas, unused imports, missed imports)
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Chris Ha 2020-02-07 19:36:18 +09:00
parent db91ba053b
commit 904c618040
1 changed files with 10 additions and 12 deletions
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timm/models/EcaModule.py
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@ -1,17 +1,17 @@
'''
ECA module from ECAnet
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
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
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
@ -31,10 +31,8 @@ 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 torch
from torch import nn
from torch.nn.parameter import Parameter
import torch.nn.functional as F
class EcaModule(nn.Module):
"""Constructs a ECA module.
@ -47,7 +45,7 @@ class EcaModule(nn.Module):
super(EcaModule, self).__init__()
assert k_size % 2 == 1
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False)
self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
# feature descriptor on the global spatial information
@ -82,11 +80,11 @@ class EcaModule(nn.Module):
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
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
(accuracy, robustness), without signficantly impacting resource metrics
(parameter size, throughput,latency, etc)
Args:
@ -100,16 +98,16 @@ class CecaModule(nn.Module):
#pytorch circular padding mode is bugged as of pytorch 1.4
# see https://github.com/pytorch/pytorch/pull/17240
#implement manual circular padding
self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding = 0, bias=False)
self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=0, bias=False)
self.padding = (k_size - 1) // 2
self.sigmoid = nn.Sigmoid()
def forward(self, x):
# feature descriptor on the global spatial information
y = self.avg_pool(x)
#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')
y = F.pad(y.view(x.shape[0], 1, -1), (self.padding, self.padding), mode='circular')
# Two different branches of ECA module
y = self.conv(y)