torch.cat() 和 torch.stack()略有不同
torch.cat(tensors,dim=0,out=None)→ Tensor
torch.cat()对tensors沿指定维度拼接,但返回的Tensor的维数不会变,可理解为续接;
torch.stack(tensors,dim=0,out=None)→ Tensor
torch.stack()同样是对tensors沿指定维度拼接,但返回的Tensor会多一维,可理解为叠加;
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版权声明:本文为CSDN博主「进阶媛小吴」的原创文章,遵循CC 4.0 BY-SA版权协议,转载请附上原文出处链接及本声明。
原文链接:https://blog.csdn.net/wuli_xin/article/details/118972316
结果为:
上述行数相同d,c,在第一维度也即列上拼接时,能拼接成100行六列的tensor.
import torch # t1=torch.tensor([1,1,1]) # t2=torch.tensor([2,2,2]) # t3=torch.tensor([3,3,3]) f1=torch.tensor([[1,2,3],[4,5,6]]) f2=torch.tensor([[7,8,9],[10,11,12]]) c=torch.tensor([[13,14,15],[16,17,18]]) # a=torch.cat((f1,f2,c),dim=0) # b=torch.cat((f1,f2,c),1) # print(a.shape,b.shape,sep=' ') # d=torch.rand(100,4) # e=torch.cat((d,c),1) # print(e.shape) g=torch.stack((f1,f2,c),0) g1=torch.stack((f1,f2,c),1) print(f1.shape,f2.shape,c.shape) print('g: ',g.shape,g,sep=' ') print('g1: ',g1.shape,g1,sep=' ') 输出结果为: torch.Size([2, 3]) torch.Size([2, 3]) torch.Size([2, 3]) g: torch.Size([3, 2, 3])#本来3个,就3个;本来2行3列就两行三列;只不过把他们放到一起,变成了3维的,多了一个维度;个人理解,可能有误。 tensor([[[ 1, 2, 3], [ 4, 5, 6]], [[ 7, 8, 9], [10, 11, 12]], [[13, 14, 15], [16, 17, 18]]]) g1: torch.Size([2, 3, 3])#把本来的三个中,每个的第一列拼在一块;第二列拼在一块;再把拼过后的第一列和第二列分别作为一个二维矩阵; 个人理解,可能有误。 tensor([[[ 1, 2, 3], [ 7, 8, 9], [13, 14, 15]], [[ 4, 5, 6], [10, 11, 12], [16, 17, 18]]])
import torch # t1=torch.tensor([1,1,1]) # t2=torch.tensor([2,2,2]) # t3=torch.tensor([3,3,3]) f1=torch.tensor([[1,2,3],[4,5,6]]) f2=torch.tensor([[7,8,9],[10,11,12]]) c=torch.tensor([[13,14,15],[16,17,18]]) # a=torch.cat((f1,f2,c),dim=0) # b=torch.cat((f1,f2,c),1) # print(a.shape,b.shape,sep=' ') # d=torch.rand(100,4) # e=torch.cat((d,c),1) # print(e.shape) g=torch.stack((f1,f2,c),0) g1=torch.stack((f1,f2,c),2) print(f1.shape,f2.shape,c.shape) print('g: ',g.shape,g,sep=' ') print('g1: ',g1.shape,g1,sep=' ') 输出结果: torch.Size([2, 3]) torch.Size([2, 3]) torch.Size([2, 3]) g: torch.Size([3, 2, 3]) tensor([[[ 1, 2, 3], [ 4, 5, 6]], [[ 7, 8, 9], [10, 11, 12]], [[13, 14, 15], [16, 17, 18]]]) g1: torch.Size([2, 3, 3]) tensor([[[ 1, 7, 13], [ 2, 8, 14], [ 3, 9, 15]], [[ 4, 10, 16], [ 5, 11, 17], [ 6, 12, 18]]])
import torch # t1=torch.tensor([1,1,1]) # t2=torch.tensor([2,2,2]) # t3=torch.tensor([3,3,3]) f1=torch.tensor([[1,2,3],[4,5,6]]) f2=torch.tensor([[7,8,9],[10,11,12]]) c=torch.tensor([[13,14,15],[16,17,18]]) # a=torch.cat((f1,f2,c),dim=0) # b=torch.cat((f1,f2,c),1) # print(a.shape,b.shape,sep=' ') # d=torch.rand(100,4) # e=torch.cat((d,c),1) # print(e.shape) g=torch.stack((f1,f2,c),0) g1=torch.stack((f1,f2,c),3)#此处dim=3,或比3大的任何正数,都是如下报错结果。 print(f1.shape,f2.shape,c.shape) print('g: ',g.shape,g,sep=' ') print('g1: ',g1.shape,g1,sep=' ') 输出结果: Traceback (most recent call last): File "<input>", line 17, in <module> IndexError: Dimension out of range (expected to be in range of [-3, 2], but got 3)
此外:
如果,torch.stack()的维度dim输入的是-1,-2,-3,也都可以正确输出结果。但是如果输入比-3小的任何数则会报错;具体如下:
import torch # t1=torch.tensor([1,1,1]) # t2=torch.tensor([2,2,2]) # t3=torch.tensor([3,3,3]) f1=torch.tensor([[1,2,3],[4,5,6]]) f2=torch.tensor([[7,8,9],[10,11,12]]) c=torch.tensor([[13,14,15],[16,17,18]]) # a=torch.cat((f1,f2,c),dim=0) # b=torch.cat((f1,f2,c),1) # print(a.shape,b.shape,sep=' ') # d=torch.rand(100,4) # e=torch.cat((d,c),1) # print(e.shape) g=torch.stack((f1,f2,c),0) g1=torch.stack((f1,f2,c),-1) #此时,维度是-1 print(f1.shape,f2.shape,c.shape) print('g: ',g.shape,g,sep=' ') print('g1: ',g1.shape,g1,sep=' ') 输出结果为: torch.Size([2, 3]) torch.Size([2, 3]) torch.Size([2, 3]) g: torch.Size([3, 2, 3]) tensor([[[ 1, 2, 3], [ 4, 5, 6]], [[ 7, 8, 9], [10, 11, 12]], [[13, 14, 15], [16, 17, 18]]]) g1: torch.Size([2, 3, 3]) tensor([[[ 1, 7, 13], [ 2, 8, 14], [ 3, 9, 15]], [[ 4, 10, 16], [ 5, 11, 17], [ 6, 12, 18]]])
torch.stack()的维度dim输入的是--2;
输出结果为: torch.Size([2, 3]) torch.Size([2, 3]) torch.Size([2, 3]) g: torch.Size([3, 2, 3]) tensor([[[ 1, 2, 3], [ 4, 5, 6]], [[ 7, 8, 9], [10, 11, 12]], [[13, 14, 15], [16, 17, 18]]]) g1: torch.Size([2, 3, 3]) tensor([[[ 1, 2, 3], [ 7, 8, 9], [13, 14, 15]], [[ 4, 5, 6], [10, 11, 12], [16, 17, 18]]])
torch.stack()的维度dim输入的是-3;
torch.Size([2, 3]) torch.Size([2, 3]) torch.Size([2, 3]) g: torch.Size([3, 2, 3]) tensor([[[ 1, 2, 3], [ 4, 5, 6]], [[ 7, 8, 9], [10, 11, 12]], [[13, 14, 15], [16, 17, 18]]]) g1: torch.Size([3, 2, 3]) tensor([[[ 1, 2, 3], [ 4, 5, 6]], [[ 7, 8, 9], [10, 11, 12]], [[13, 14, 15], [16, 17, 18]]])
import torch # t1=torch.tensor([1,1,1]) # t2=torch.tensor([2,2,2]) # t3=torch.tensor([3,3,3]) f1=torch.tensor([[1,2,3],[4,5,6]]) f2=torch.tensor([[7,8,9],[10,11,12]]) c=torch.tensor([[13,14,15],[16,17,18]]) # a=torch.cat((f1,f2,c),dim=0) # b=torch.cat((f1,f2,c),1) # print(a.shape,b.shape,sep=' ') # d=torch.rand(100,4) # e=torch.cat((d,c),1) # print(e.shape) g=torch.stack((f1,f2,c),0) g1=torch.stack((f1,f2,c),-4)#此处是dim=-4,小于-4的任何负数,输出类似的结果。 print(f1.shape,f2.shape,c.shape) print('g: ',g.shape,g,sep=' ') print('g1: ',g1.shape,g1,sep=' ') 输出结果为: Traceback (most recent call last): File "<input>", line 17, in <module> IndexError: Dimension out of range (expected to be in range of [-3, 2], but got -4)