sparse_coo_tensor(torch.empty([1, 0]), [], [1]) tensor(indices=tensor([], size=(1, 0)), values=tensor([], size=(0,)), size=(1,), nnz=0, layout=torch.sparse_coo) # and to create an empty sparse tensor with nnz = 0, dense_dim = 1 and # sparse_dim = 1 >>> S ...
filter_mask[i] =1filter_mask = sparse.COO(coords=mt.coords, data=filter_mask, shape=mt.shape)iffilter_maskisnotNone:# We can pass the sparse filter mask directlymt = sparse.where(filter_mask, mt, mt.dtype.type(0))return_get_mult_function(mt)else:return_get_mult_function_runtime_spar...
pytorch 中稀疏矩阵和 dense 矩阵互转 import torch index = torch.Tensor([[0, 1, 2], [1, 2, 3]]) # index:shape为[2,n],代表着行下标向量和列下标向量 value = torch.ones((index.shape[1])) # value:shape为[n],代表着非零元素的值 N = 4 # 节点总数 adj = torch.sparse_coo_tensor(...
针对你遇到的 RuntimeError: permute(sparse_coo): number of dimensions in the tensor input 错误,以下是一些可能的解决步骤和解释: 1. 错误含义 这个错误表明你在对一个稀疏COO(Coordinate Format)格式的张量执行 permute 操作时,输入的张量维度数目不符合 permute 函数的要求。permute 函数需要指定一个新的维度顺...
sparse_coo_npu, sparse_coo_cpu = self._create_sparse_coo_tensor() self.assertRtolEqual(sparse_coo_npu._nnz(), sparse_coo_cpu._nnz()) def test_sparse_coo_indices_and_values(self): sparse_coo_npu, sparse_coo_cpu = self._create_sparse_coo_tensor() self.assertRtolEqual(sparse_co...
sparse_coo_tensor( new_indices, sparse._values(), torch.Size((*[1 for _ in range(num_new_dims)], *sparse.shape)), dtype=sparse.dtype, device=sparse.device, ) for i, repeat_size in enumerate(repeat_sizes): if repeat_size > 1: new_indices = sparse._indices().repeat(1, repeat_...
我通过在Tensor上使用squeeze（）操作来匹配所需的维度长度3来解决它，其中输入为4。我首先检查了输入...
在科学计算库Scipy中,scipy.sparse.coo_matrix是一个非常重要的模块,它提供了用于处理稀疏矩阵的COO(Compressed Sparse Row)格式。稀疏矩阵,作为一种存储效率较高的矩阵类型,在实际应用中被广泛使用。特别是在需要处理大量零元素的情况下,采用稀疏矩阵可以大大节省内存占用。
Transposing a highly sparse (less than 0.0001% nonzero) COO tensor takes significantly (around 50x) longer than transposing the same tensor in dense format. Below is a code snippet: import torch import timeit with torch.no_grad(): device = "cuda" dim0 = 30 dim1 = 120000 arr1 = torch...
文章目录 1. tensor 张量 2. 运算 3. 切片、形状size()、改变形状view() 4. item() 只能读取一...