但后者是PyTorch的一个子模块。所以，看看正确的包（torch_sparse），那里没有太多关于如何使用SparseTensor类的信息（Link），如果我们看另一方面的源代码（Link），你可以看到这个类有一堆classmethod，你可以用它们从文档很好的pytorch类生成你自己的SparseTensor。在我的例子中，我所需要的只是一种方法来为RGCNConv层提供一个包含边和边类型的Tensor，...
conda install -c pytorch torch-sparse 确认torch_sparse库的导入方式是否正确: 正确的导入方式应该是: python import torch_sparse 你提到的 from torch_sparse import sparsetensor 是不正确的,因为torch_sparse库中没有直接名为sparsetensor的模块或函数。如果你需要创建一个稀疏张量,应该使用torch_sparse库提供...
FileNotFoundError: Could not find module 'E:\study\py-dev\torch-geo\venv\Lib\site-packages\torch_sparse\_convert_cpu.pyd' (or one of its dependencies). Try using the full path with constructor syntax Environment OS: Windows 10 Python version: Python 3.8.x PyTorch version: 1.9.0 CUDA/cu...
Hello thank you for the great package, I tried to install the package following the installation instructions and everything went great besides it using this wheel: torch_sparse-0.6.13-cp38-cp38-linux_x86_64.whl which leads to an error m...
按照官网步骤安装完torch-scatter、torch-sparse、torch-cluster和torch-spline-conv等依赖项,也成功安装了torch_geometric,但在导入的时候还是报错: 原因是没有C++环境,在该网址中https://visualstudio.microsoft.com/visual-cpp-build-tools/下载并安装C/C++ DLL 动态链接库,即可成功使用torch_geometric...
import torch indices = torch.LongTensor([[0,0], [1,1], [2,2]])#稀疏矩阵中非零元素的坐标 indices = indices.t() #一定要转置,因为后面sparse.FloatTensor的第一个参数就是该变量,要求是一个含有两个元素的列表,每个元素也是一个列表。第一个子列表是非零元素所在的行,第二个子列表是非零元素所在...
import torch.optim._multi_tensor from torch import multiprocessing as multiprocessing from torch import sparse as sparse from torch import special as special import torch.utils.backcompat from torch import onnx as onnx from torch import jit as jit ...
torch.manual_seed(0) data_type = torch.float16 @lru_cache def create_block_mask_from_score_mod(score_mod, B, H, M, N, device='cuda'): SPARSE_BLOCK = 128 block_mask = _create_block_mask(score_mod, B, H, M, N, device=device) ...
"""./bin/neo4j-admin.bat import --nodes "import\neo4j_nodes_admin.csv" --relationships "import\neo4j_edges_admin.csv"注意:java11,"""fromutilsimportXYGraphP1importtorch_geometric.transformsasTimportnumpyasnpimportpandasaspdimportpickledefto_pickle(filename,obj):withopen(filename,'wb')asf:pickle....
torch.zeros_like, torch.range, torch.sparse_csr_tensor, torch.randn_like, torch.from_file, torch._cudnn_init_dropout_state, torch._empty_affine_quantized, torch.linspace, torch.hamming_window, torch.empty_quantized, torch._pin_memory, torch.autocast, torch.Tensor.new_empty, torch.Tensor.new...