这里看fuse_shuffle_channel,源码如下: static void fuse_shufflechannel(onnx::GraphProto* mutable_graph, std::map<std::string, onnx::TensorProto>& weights, std::map<std::string, int>& node_reference, std::set<std::string>& blob_names, int& reduced_node_count) { int node_count = mutab...
voidShuffleChannelLayer<Dtype>::Backward_gpu(constvector<Blob<Dtype>*>& top, constvector<bool>& propagate_down, constvector<Blob<Dtype>*>& bottom) { if(propagate_down[0]) { constDtype* top_diff = top[0]->gpu_diff(); Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); ...
shuffle channel 实现 欲庐骄子 import tensorflow as tf def channel_shuffle(feature, group): channel_num = feature.shape[-1] if channel_num % group != 0: raise ValueError("The group must be divisible by the shape of the last dimension of the feature.") x = tf.reshape(feature, shape=(...
voidShuffleChannelLayer<Dtype>::Backward_cpu(constvector<Blob<Dtype>*>& top, constvector<bool>& propagate_down, constvector<Blob<Dtype>*>& bottom) { if(propagate_down[0]) { constDtype* top_diff = top[0]->cpu_diff(); Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); ...
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, ...
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....
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...
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...
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...
您好,我在rknn1808上运行以shufflenetv2为backbone的nanodet模型时(模型未量化),纯网络输出的数据维度是对的,但数值不对,数值是与ncnn以及rknn在pc上推理时的结果进行对比的。而且,在运行时有如下提示:因此,想请问,rknn是否支持shufflenet中的shufflechannel层对数据的五维操作。