建设网站计划书,电商平台需要什么资质,无锡万度网站建设,编程自己做网站ODConv动态卷积模块
ODConv可以视作CondConv的延续#xff0c;将CondConv中一个维度上的动态特性进行了扩展#xff0c;同时了考虑了空域、输入通道、输出通道等维度上的动态性#xff0c;故称之为全维度动态卷积。ODConv通过并行策略采用多维注意力机制沿核空间的四个维度…ODConv动态卷积模块
ODConv可以视作CondConv的延续将CondConv中一个维度上的动态特性进行了扩展同时了考虑了空域、输入通道、输出通道等维度上的动态性故称之为全维度动态卷积。ODConv通过并行策略采用多维注意力机制沿核空间的四个维度学习互补性注意力。作为一种“即插即用”的操作它可以轻易的嵌入到现有CNN网络中。ImageNet分类与COCO检测任务上的实验验证了所提ODConv的优异性即可提升大模型的性能又可提升轻量型模型的性能实乃万金油是也值得一提的是受益于其改进的特征提取能力ODConv搭配一个卷积核时仍可取得与现有多核动态卷积相当甚至更优的性能。
原文地址Omni-Dimensional Dynamic Convolution 代码实现
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.autograd
from models.common import Conv, autopadclass Attention(nn.Module):def __init__(self, in_planes, out_planes, kernel_size, groups1, reduction0.0625, kernel_num4, min_channel16):super(Attention, self).__init__()attention_channel max(int(in_planes * reduction), min_channel)self.kernel_size kernel_sizeself.kernel_num kernel_numself.temperature 1.0self.avgpool nn.AdaptiveAvgPool2d(1)self.fc Conv(in_planes, attention_channel, actnn.ReLU(inplaceTrue))self.channel_fc nn.Conv2d(attention_channel, in_planes, 1, biasTrue)self.func_channel self.get_channel_attentionif in_planes groups and in_planes out_planes: # depth-wise convolutionself.func_filter self.skipelse:self.filter_fc nn.Conv2d(attention_channel, out_planes, 1, biasTrue)self.func_filter self.get_filter_attentionif kernel_size 1: # point-wise convolutionself.func_spatial self.skipelse:self.spatial_fc nn.Conv2d(attention_channel, kernel_size * kernel_size, 1, biasTrue)self.func_spatial self.get_spatial_attentionif kernel_num 1:self.func_kernel self.skipelse:self.kernel_fc nn.Conv2d(attention_channel, kernel_num, 1, biasTrue)self.func_kernel self.get_kernel_attentionself._initialize_weights()def _initialize_weights(self):for m in self.modules():if isinstance(m, nn.Conv2d):nn.init.kaiming_normal_(m.weight, modefan_out, nonlinearityrelu)if m.bias is not None:nn.init.constant_(m.bias, 0)if isinstance(m, nn.BatchNorm2d):nn.init.constant_(m.weight, 1)nn.init.constant_(m.bias, 0)def update_temperature(self, temperature):self.temperature temperaturestaticmethoddef skip(_):return 1.0def get_channel_attention(self, x):channel_attention torch.sigmoid(self.channel_fc(x).view(x.size(0), -1, 1, 1) / self.temperature)return channel_attentiondef get_filter_attention(self, x):filter_attention torch.sigmoid(self.filter_fc(x).view(x.size(0), -1, 1, 1) / self.temperature)return filter_attentiondef get_spatial_attention(self, x):spatial_attention self.spatial_fc(x).view(x.size(0), 1, 1, 1, self.kernel_size, self.kernel_size)spatial_attention torch.sigmoid(spatial_attention / self.temperature)return spatial_attentiondef get_kernel_attention(self, x):kernel_attention self.kernel_fc(x).view(x.size(0), -1, 1, 1, 1, 1)kernel_attention F.softmax(kernel_attention / self.temperature, dim1)return kernel_attentiondef forward(self, x):x self.avgpool(x)x self.fc(x)return self.func_channel(x), self.func_filter(x), self.func_spatial(x), self.func_kernel(x)class ODConv2d(nn.Module):def __init__(self, in_planes, out_planes, k, s1, pNone, g1, actTrue, d1,reduction0.0625, kernel_num1):super(ODConv2d, self).__init__()self.in_planes in_planesself.out_planes out_planesself.kernel_size kself.stride sself.padding autopad(k, p)self.dilation dself.groups gself.kernel_num kernel_numself.attention Attention(in_planes, out_planes, k, groupsg,reductionreduction, kernel_numkernel_num)self.weight nn.Parameter(torch.randn(kernel_num, out_planes, in_planes//g, k, k),requires_gradTrue)self._initialize_weights()self.bn nn.BatchNorm2d(out_planes)self.act nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())if self.kernel_size 1 and self.kernel_num 1:self._forward_impl self._forward_impl_pw1xelse:self._forward_impl self._forward_impl_commondef _initialize_weights(self):for i in range(self.kernel_num):nn.init.kaiming_normal_(self.weight[i], modefan_out, nonlinearityrelu)def update_temperature(self, temperature):self.attention.update_temperature(temperature)def _forward_impl_common(self, x):# Multiplying channel attention (or filter attention) to weights and feature maps are equivalent,# while we observe that when using the latter method the models will run faster with less gpu memory cost.channel_attention, filter_attention, spatial_attention, kernel_attention self.attention(x)batch_size, in_planes, height, width x.size()x x * channel_attentionx x.reshape(1, -1, height, width)aggregate_weight spatial_attention * kernel_attention * self.weight.unsqueeze(dim0)aggregate_weight torch.sum(aggregate_weight, dim1).view([-1, self.in_planes // self.groups, self.kernel_size, self.kernel_size])output F.conv2d(x, weightaggregate_weight, biasNone, strideself.stride, paddingself.padding,dilationself.dilation, groupsself.groups * batch_size)output output.view(batch_size, self.out_planes, output.size(-2), output.size(-1))output output * filter_attentionreturn outputdef _forward_impl_pw1x(self, x):channel_attention, filter_attention, spatial_attention, kernel_attention self.attention(x)x x * channel_attentionoutput F.conv2d(x, weightself.weight.squeeze(dim0), biasNone, strideself.stride, paddingself.padding,dilationself.dilation, groupsself.groups)output output * filter_attentionreturn outputdef forward(self, x):return self.act(self.bn(self._forward_impl(x)))
