torch.index_select:通过选择索引然后去得到想要的tensor,针对比较长的tensor torch.index_select(tensor, 维度,选择的index) 代码示例: importtorch#shape为(2,2,3)a=torch.tensor([[[1,2,3],[4,5,6]],[[7,8,9],[10,11,12]]])#选择索引0和索引2的tensorindices=torch.tensor([0,2])#tensor为a...
>>>x=torch.randn(3,4)>>>xtensor([[0.1427,0.0231,-0.5414,-1.0009],[-0.4664,0.2647,-0.1228,-1.1068],[-1.1734,-0.6571,0.7230,-0.6004]])>>>indices=torch.tensor([0,2])>>>torch.index_select(x,0,indices)tensor([[0.1427,0.0231,-0.5414,-1.0009],[-1.1734,-0.6571,0.7230,-0.6004]])>>>t...
torch.index_select(input, dim, index, *, out=None) → Tensor 作用是: Returns a new tensor which indexes the input tensor along dimension dim using the entries in index which is a LongTensor. 返回按照相应维度的给定index的选取的元素,index必须是longtensor。 按照dim=0时, x_0应该是在4块中...
torch.index_select(input,dim,index,out=None) → Tensor Returns a new tensor which indexes theinputtensor along dimensiondimusing the entries inindexwhich is a LongTensor. The returned tensor has the same number of dimensions as the original tensor (input). Thedimth dimension has the same size...
torch.select 主要由dim、index两个因子来控制取数据的粒度,而TensorRT 在用 Slice 去切 Tensor 的时候,一般由start、size、stride三个因子来控制切的粒度。这样,其实 select 的dim和index完全可以转换为 Slice 的三个因子去控制。 来用代码进行讲解: /// 以下是 explicit 模式的写法,explicit 模式需要考虑 batch...
torch.index_select(input,dim,index,out=None) → Tensor Returns a new tensor which indexes theinputtensor along dimensiondimusing the entries inindexwhich is a LongTensor. The returned tensor has the same number of dimensions as the original tensor (input). Thedimth dimension has the same size...
requires_grad=True 要求计算梯度 requires_grad=False 不要求计算梯度 with torch.no_grad()或者@...
TypeError: torch.index_select received an invalid combination of arguments - got (torch.cuda.FloatTensor, int, torch.LongTensor), but expected (torch.cuda.FloatTensor source, int dim, torch.cuda.LongTensor index) Could you help? clairettclosed this ascompletedApr 17, 2018...
6.张量拼接:使用torch.cat函数将多个张量拼接在一起。例如,torch.cat((a, b), 0)将沿着第0维将张量a和b拼接在一起。 7.张量索引:使用torch.Tensor对象的index_select方法对张量进行索引。例如,a.index_select(0, torch.tensor([1, 2, 3]))将返回张量a中第1、2、3个元素组成的向量。©...
print("最后一列数据:{}".format(index_test[:,-1])) # 索引 mask =index_test>0 #根据原始数据建立索引 print(torch.masked_select(index_test,mask)) #根据索引获取值 print(torch.nonzero(mask)) #获取非零数值的下标 # torch.gather——按照规定的格式、方式进行索引 ...