sparse_dim + dense_dim = len(SparseTensor.shape) SparseTensor._indices().shape = (sparse_dim, nnz) SparseTensor._values().shape = (nnz, SparseTensor.shape[sparse_dim:]) 因为SparseTensor._indices()总是一个二维张量, 最小的sparse_dim = 1. 因此, sparse_dim = 0的稀疏张量的表示就是一个...
sparse_coo_tensor(eye, torch.ones([num_nodes]), size) adj = adj.t() + adj + eye # greater than 1 when edge_index is already symmetrical adj = adj.to_dense().gt(0).to_sparse().type(torch.float) return adj Example #2Source File: alignment.py From OpenNMT-py with MIT License ...
as_tensor([x1, y1, x2, y2]) # 首先,将每个尺度的标签数据的shape从 [B, H, W, A, C] 的形式reshape成 [B, M, C] ,其中M = HWA,以便后续的处理 # 然后,将所有尺度的预测拼接在一起,方便后续的损失计算 gt_objectness = torch.cat([gt.view(bs, -1, 1) for gt in gt_objectness], ...
torch tensor reshape和resize 在PyTorch 中,`reshape` 和 `resize` 都是用来改变张量(Tensor)形状的函数,它们的具体实现有一些不同。 - `reshape` 函数是将原始张量的数据重新排列,以得到一个具有新形状的张量。这个新形状必须与原始张量包含的元素数量相同,否则将会抛出异常。这个函数的实现是基于底层数据的视图...
new_zeros(size, dtype=None, device=None, requires_grad=False) → Tensor is_cuda device grad T abs() → Tensor abs_() → Tensor acos() → Tensor acos_() → Tensor add(value) → Tensor add_(value) → Tensor add_(value=1, other) -> Tensor ...
sparse_coo_tensor( indices, sparse._values(), torch.Size((batch_size * num_rows, batch_size * num_cols)), dtype=sparse._values().dtype, device=sparse._values().device, ) dense_2d = dense.reshape(batch_size * num_cols, -1) res = torch.dsmm(sparse_2d, dense_2d) res = res....
注意:在进行Tensor操作时,有些操作如transpose()、permute()等可能会把Tensor在内存中变得不连续,而有些操作如view()等是需要Tensor内存连续的,这种情况下需要使用contiguous()操作先将内存变为连续的。在PyTorch v0.4版本中增加了reshape()操作,可以看做是Tensor.contiguous().view() ...
>>>nnz=3>>>dims=[5,5,2,3]>>>I=torch.cat([torch.randint(0,dims[0],size=(nnz,)),torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz)>>>V=torch.randn(nnz,dims[2],dims[3])>>>size=torch.Size(dims)>>>S=torch.sparse_coo_tensor(I,V,size)>>>Stensor(indices...
1.1 reshape 1.2 squeezing and unsqueezing 1.3 flatten a tensor 1.4 concatenating tensors: terch.cat/torch.stack Element-wise operations Reduction operations Access operations 1. Stack vs Cat in PyTorch torch.cat和torch.stack都是张量拼接相关的操作,二者有什么不同?
I'm getting an error when importing torch_sparse. I have done a fresh installation of torch, with version 1.4.0. This is in order to get torch_geometric up to date. But I'm running into an error here: >>> import torch_sparse Traceback (m...