在torch_geometric库中,torch_geometric.loader模块是专门用于加载图数据集的。根据你提供的信息,我会先检查该模块中是否存在dataloader类或函数,然后解释其功能和用法,如果不存在则提供正确的类或函数。 检查torch_geometric.loader模块: 在torch_geometric的官方文档中,torch_geometric.loader模块确实包含用于加载图数据集...
Installing geometric torch to process our complex model, version_nums = torch.__version__.split('.') # Torch Geometric seems to always build for *.*.0 of torch : version_nums[-1] = '0' + version_nums[-1][1:] os.environ['TORCH'] = '.'.join(version_nums) !pip install --upgr...
#如何构建数据集fromtorch_geometric.dataimportInMemoryDatasetfromtqdmimporttqdm#进度条classYooChooseBinaryDataset(InMemoryDataset):def__init__(self, root, transform=None, pre_transform=None):#构造函数super(YooChooseBinaryDataset, self).__init__(root, transform, pre_transform)# transform就是数据增强,对...
fromtorch_geometric.datasetsimportPlanetoid#下载数据集fromtorch_geometric.transformsimportNormalizeFeatures dataset = Planetoid(root='data/Planetoid', name='Cora', transform=NormalizeFeatures())#transform预处理print()print(f'Dataset:{dataset}:')print('===')print(f'Number of graphs:{len(dataset)}')#...
from torch_geometric.nn import SchNet parser = argparse.ArgumentParser() parser.add_argument('--cutoff', type=float, default=10.0, help='Cutoff distance for interatomic interactions') args = parser.parse_args() path = osp.join(osp.dirname(osp.realpath(file)), '..', 'data', 'QM9') ...
geometric.data和类torch_geometric.data.Data弄混了。Data类具有属性from_dict/to_dict，但data模块没有...
🐛 Describe the bug transformers.cache_utils.DynamicCache inherits from torch.nn.Module. It seems to confuse torch.export.export and gives the following error: File ".../site-packages/torch/export/_trace.py", line 1697, in _export_to_aten...
geometric.data和类torch_geometric.data.Data弄混了。Data类具有属性from_dict/to_dict，但data模块没有...
test_loader = torch.utils.data.DataLoader( datasets.CIFAR10('deeprobust/image/data', train =False, download=True, transform = transform_val), batch_size =10, shuffle=True) x, y = next(iter(test_loader)) x = x.to('cuda').float() ...
本文根据传入的用户对电影的评分以及电影的类型数据,首先电影和用户作为图的点,根据电影的类型作为电影的特征,用户则通过embedding映射成向量作为特征,用户对电影的评分作为边,再通过torch_geometric把单向边转换为双向边,也就是异构图(点的类型多种,边的类型多种)。最后,传入GNN网络,对每一个点做预测,越接近1表示用...