CNN中的通道混洗Channel shuffle是什么 (Channel Shuffle)是一种重新排列特征通道的方法,它的作用是增强特征信息流动,提高模型的表达能力。 简单来说,它的核心步骤是: 先把通道分成多个组(groups) 在组内交换通道的顺序(也可以理解成是交换特征的位置) 最后恢复原来的形状 这样可以让不同通道之间的信息更多地交互,...
importtorchdefchannel_shuffle(x,groups):batchsize,num_channels,height,width=x.data.size()channels_per_group=num_channels//groups# num_channels = groups * channels_per_group# grouping, 通道分组# b, num_channels, h, w ===> b, groups, channels_per_group, h, wx=x.view(batchsize,groups,...
channel_shuffle¶ dragon.nn.channel_shuffle( inputs, axis=- 1, group=1, **kwargs )[source]¶ Applythegroupshuffletoeachchannelofinput.[Zhanget.al,2017]. Examples: x=dragon.constant([1,2,3,4])print(dragon.nn.channel_shuffle(x,group=2))# [1, 3, 2...
Shuffle Net的Channel Shuffle模块是咋回事? 看一下这个图片, [a]就是普通的分组卷积,比如(M,M,16)的feature map按channel分成4组,每组(M,M,4),每组用K个(3,3,4)的卷积核去卷积,这样就能得到4个feature map(如果加了padding使大小不变的话,就是4个(M,M,K)的feature map),这样(M,M,16)=>(M,M,...
nn as nn class Channel_Shuffle(nn.Module): def __init__(self, num_groups): super(Channel_Shuffle, self).__init__() self.num_groups = num_groups def forward(self, x: torch.FloatTensor): batch_size, chs, h, w = x.shape chs_per_group = chs // self.num_groups x = torch....
ChannelShuffle(2) >>> input = torch.randn(1, 4, 2, 2) >>> print(input) [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]], [[13, 14], [15, 16]], ]] >>> output = channel_shuffle(input) >>> print(output) [[[1, 2], [3, 4]], [[9, ...
1762 目标检测 模型数量 879 语义分割 模型数量 771 姿态估计 模型数量 391 行人重识别 模型数量 203 二维人体姿态估计 模型数量 135 网络剪枝 模型数量 20 模型压缩 模型数量 8 使用「Channel Shuffle(Channel Shuffle)」的项目 Lite-HRNet-18 Changqian Yu 等7人 ...
Channel Shuffle is an operation to help information flow across feature channels in convolutional neural networks. It was used as part of the ShuffleNet architecture. If we allow a group convolution to obtain input data from different groups, the input and output channels will be fully related. ...
Lightweight video salient object detection via channel-shuffle enhanced multi-modal fusion networkVideo salient object detectionLightweight modelMulti-modal fusionVideo salient object detection (VSOD) has witnessed great development with the applicationof deep neural networks. However, the high computational...
Designer > Substance compositing graphs > Nodes reference for Substance compositing graphs > Atomic nodes > Channel Shuffle