自定义collate_fn与torch_geometric不兼容的问题 最近在跑一个轨迹预测的训练部署,去掉了所有torch_geometric相关的依赖。然而在用自定义collate_fn函数的时候,发现没有起作用。 经过排查后原因是,dataloader里面的这个依赖忘记去除了。 from torch_geometric.loader import DataLoader # 这里面不支持collate_fn from torch...
1. 使用多线程调试:可以使用多线程调试技术监测Dataloader的执行情况,找出具体的死锁点。 数据加载线程主线程数据加载线程主线程可能会在此处出现死锁请求数据返回数据请求数据返回数据等待数据输出再返回 结论 在使用PyTorch Dataloader和torch_geometric进行模型训练时,避免死锁的问题主要在于合理设置Dataloader的参数,以及在出...
Hello, when I use the command”from torch_geometric.loader import DataLoader“ , I will report an error”ModuleNotFoundError: No module named 'torch_geometric.loader“. What's the problem? thanks a lot. Environment PyG version:1.7.1
通过torch_geometric.data.DataLoader可以方便地使用 mini-batch。 fromtorch_scatterimportscatter_meanfromtorch_geometric.datasetsimportTUDatasetfromtorch_geometric.loaderimportDataLoaderdataset=TUDataset(root='/tmp/ENZYMES',name='ENZYMES',use_node_attr=True)loader=DataLoader(dataset,batch_size=32,shuffle=True)fo...
astr = 'hello' alist = [10, 20, 30] atuple = ('bob', 'tom', 'alice') adict = {'...
例如,可以使用torch_geometric中的批处理函数将数据集转换为批次形式,方便进行批次训练: ```python from torch_geometric.data import Batch #转换为批次形式 train_loader = torch_geometric.data.DataLoader(train_dataset, batch_size=64, shuffle=True) test_loader = torch_geometric.data.DataLoader(test_dataset...
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Data loader Each of the training and test sets gets their own data loader: train_dl <- dataloader(train_ds, batch_size = 32, shuffle = TRUE) test_dl <- dataloader(test_ds, batch_size = 32) Again,torchmakes it easy to verify we did the correct thing. To take a look at the conte...
import torch.nn.functional as F fromtorch_geometric.data import Data,DataLoader from torch_geometric.nn import GCNConv from torch_scatter import scatter_max class SmallNet(torch.nn.Module): def __init__(self): super(SmallNet, self).__init__() ...
loader import DataLoader from torch_geometric.nn import ( GlobalAttention, @@ -520,7 +521,7 @@ def run(rank, dataset, args): checkpoint_path = os.path.join(args.checkpoint_dir, 'checkpoint.pt') if os.path.isfile(checkpoint_path): checkpoint = torch.load(checkpoint_path) checkpoint = ...