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...
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 = nn.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,...
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....
self.avg_pool1d = nn.AvgPool1d(kernel_size=2) self.avg_pool3d = nn.AvgPool3d(kernel_size=(2, 2, 2)) self.channel_shuffle = nn.ChannelShuffle(groups=3) def forward(self, x): device = x.device x = x.view(-1, 3, 32 * 32) ...
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 ...
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...
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. ...
shufflenet中channel shuffle原理 分组卷积 Group convolution是将输入层的不同特征图进行分组,然后采用不同的卷积核再对各个组进行卷积,这样会降低卷积的计算量。因为一般的卷积都是在所有的输入特征图上做卷积,可以说是全通道卷积,这是一种通道密集连接方式(channel dense connection),而group convolution相比则是一种...