from torch.utils.data.sampler import RandomSampler, SequentialSampler, SubsetRandomSampler, WeightedRandomSampler # 创建一个数据集 dataset = torch.utils.data.TensorDataset(torch.randn(10, 3), torch.randint(0, 2, (10,))) # 创建一个使用RandomSampler的DataLoader random_loader = DataLoader(dataset, ...
from data.dataloader import get_test_loader File "/home/nvidia/Ultra-Fast-Lane-Detection-v2/data/dataloader.py", line 4, in import torchvision.transforms as transforms File "/home/nvidia/.local/lib/python3.8/site-packages/torchvision/init.py", line 5, in from torchvision import datasets, io,...
最后,我们通过迭代data_loader来访问每个批次的数据。 总结: 如果你在代码中看到from torch_geometric.loader import dataloader,这可能是由于命名习惯或版本差异。在大多数情况下,你应该使用DataLoader类来加载图数据集。上述示例展示了如何使用DataLoader来加载和迭代图数据集。
# 需要导入模块: from ansible.parsing.dataloader import DataLoader [as 别名]# 或者: from ansible.parsing.dataloader.DataLoader importload_from_file[as 别名]defmain(self, path):data_dir = self.conf['data_dir'] loader = DataLoader() full_path="%s/%s"% (data_dir, path)ifos.path.isfile("%...
fromtorch_geometric.loaderimportDataLoaderdeftrain():#训练model.train() loss_all =0fordataintrain_loader:#遍历data = data#拿到每个数据#print('data',data)optimizer.zero_grad() output = model(data)#传入数据label = data.y#拿到标签loss = crit(output, label)#计算损失loss.backward()#反向传播loss...
DataLoader(dataset, batch_size=2, sampler=weighted_sampler)# 使用BatchSampler将样本索引分成多个批次batch_sampler = torch.utils.data.sampler.BatchSampler(SequentialSampler(dataset), batch_size=2, drop_last=False)batch_loader = DataLoader(dataset, batch_sampler=batch_sampler)# 遍历DataLoader,输出每个...
from torch.utils.data import DataLoader num_workers = 0 batch_size = 8 torch.manual_seed(123) train_loader = DataLoader( dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers, drop_last=True, ) val_loader = DataLoader( dataset=val_dataset, batch_size=batch_...
import wandb # 1. Start a new run run = wandb.init(project="gpt4") # 2. Save model inputs and hyperparameters config = run.config config.dropout = 0.01 # 3. Log gradients and model parameters run.watch(model) for batch_idx, (data, target) in enumerate(train_loader): ... if bat...
read() from previous_chapters import create_dataloader_v1 # Train/validation ratio train_ratio = 0.90 split_idx = int(train_ratio * len(text_data)) torch.manual_seed(123) train_loader = create_dataloader_v1( text_data[:split_idx], batch_size=2, max_length=GPT_CONFIG_124M["context_...
DATADIR = '/home/aistudio/work/ILSVRC2012_val/' VAL_FILE_LIST = '/home/aistudio/work/val_list.txt' # 创建数据读取类 val_dataset = CommonDataset(DATADIR, VAL_FILE_LIST) # 使用paddle.io.DataLoader创建数据读取器,并设置batchsize,进程数量num_workers等参数 val_loader = paddle.io.DataLoader(...