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| """ | |
| ECA module from ECAnet | |
| paper: ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks | |
| https://arxiv.org/abs/1910.03151 | |
| Original ECA model borrowed from https://github.com/BangguWu/ECANet | |
| Modified circular ECA implementation and adaption for use in timm package | |
| by Chris Ha https://github.com/VRandme | |
| Original License: | |
| 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 | |
| class EcaModule(nn.Module): | |
| """Constructs an ECA module. | |
| Args: | |
| channels: 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.) | |
| kernel_size: Adaptive selection of kernel size (default=3) | |
| """ | |
| def __init__(self, channels=None, kernel_size=3, gamma=2, beta=1): | |
| super(EcaModule, self).__init__() | |
| assert kernel_size % 2 == 1 | |
| if channels is not None: | |
| t = int(abs(math.log(channels, 2) + beta) / gamma) | |
| kernel_size = max(t if t % 2 else t + 1, 3) | |
| self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=(kernel_size - 1) // 2, bias=False) | |
| def forward(self, x): | |
| y = x.mean((2, 3)).view(x.shape[0], 1, -1) # view for 1d conv | |
| y = self.conv(y) | |
| y = y.view(x.shape[0], -1, 1, 1).sigmoid() | |
| return x * y.expand_as(x) | |
| class CecaModule(nn.Module): | |
| """Constructs a circular ECA module. | |
| ECA module where the conv uses circular padding rather than zero padding. | |
| Unlike the spatial dimension, the channels 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 significantly impacting resource metrics | |
| (parameter size, throughput,latency, etc) | |
| Args: | |
| channels: 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.) | |
| kernel_size: Adaptive selection of kernel size (default=3) | |
| """ | |
| def __init__(self, channels=None, kernel_size=3, gamma=2, beta=1): | |
| super(CecaModule, self).__init__() | |
| assert kernel_size % 2 == 1 | |
| if channels is not None: | |
| t = int(abs(math.log(channels, 2) + beta) / gamma) | |
| kernel_size = max(t if t % 2 else t + 1, 3) | |
| # PyTorch circular padding mode is buggy 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=kernel_size, padding=0, bias=False) | |
| self.padding = (kernel_size - 1) // 2 | |
| def forward(self, x): | |
| y = x.mean((2, 3)).view(x.shape[0], 1, -1) | |
| # Manually implement circular padding, F.pad does not seemed to be bugged | |
| y = F.pad(y, (self.padding, self.padding), mode='circular') | |
| y = self.conv(y) | |
| y = y.view(x.shape[0], -1, 1, 1).sigmoid() | |
| return x * y.expand_as(x) | |