分类:Python
需求:统计列表list1中元素3的个数,并返回每个元素的索引list1=[3,3,8,9,2,10,6,2,8,3,4,5,5,4,1,5,9,7,10,2]在实际工程中,可能会遇到以上需求,统计元素个数使用list.count()方法即可,不做多余说明返回每个元素的索引需要做一些转换,简单整理了几个实现方法1list.index()方法list.index()方法返回列表中首个元素的索引,当有重复元素时,可以通过更改index()方法__start参数来更改起始索引找到一个元素...
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2021
06-12
2021
06-12
2021
06-12
2021
06-12
一、数据生成1.1手写数组a=np.array([1,2,3,4,5,6,7,8,9,10,11])#一维数组b=np.array([[1,2],[3,4]])#二维数组1.2序列数组numpy.arange(start,stop,step,dtype),start默认0,step默认1c=np.arange(0,10,1,dtype=int)#=np.arange(10)[0123456789]d=np.array([np.arange(1,3),np.arange(4,6)])#二维数组#不过为了避免麻烦,通常序列二维数组都是通过reshape进行重新组织dd=...
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一、model.py1.1ChannelShuffledefchannel_shuffle(x:Tensor,groups:int)->Tensor:batch_size,num_channels,height,width=x.size()channels_per_group=num_channels//groups#reshape#[batch_size,num_channels,height,width]->[batch_size,groups,channels_per_group,height,width]x=x.view(batch_size,groups,channels_per_group,height,width)x=torch.transpose(x,...
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一、model.py1.1ChannelShuffledefchannel_shuffle(x:Tensor,groups:int)->Tensor:batch_size,num_channels,height,width=x.size()channels_per_group=num_channels//groups#reshape#[batch_size,num_channels,height,width]->[batch_size,groups,channels_per_group,height,width]x=x.view(batch_size,groups,channels_per_group,height,width)x=torch.transpose(x,...
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