然后再调用的时候使用DataLoader加载数据 train_data=ElementDataset(args.Train) test_data=ElementDataset(args.Test) train_iter=DataLoader(dataset=train_data, batch_size=10, shuffle=True, drop_last=True, collate_fn=collate_fn) test_iter =DataLoader(dataset=test_data, batch_size=10, shuffle=True, d...
strat = time.time() 1.编写训练函数 def train(dataloader, model, loss_fn, optimizer): size = len(dataloader.dataset) # 训练集的大小 num_batches = len(dataloader) # 批次数目, (size/batch_size,向上取整) train_loss, train_acc = 0, 0 # 初始化训练损失和正确率 for X, y in dataloader:...
MpDeviceLoader(dataloader, device) Checkpoint Writing and Loading When a tensor is checkpointed from a XLA device and then loaded back from the checkpoint, it will be loaded back to the original device. Before checkpointing tensors in your model, you want to ensure that all of your...
import sys class TailRecurseException: def __init__(self, args, kwargs): self.args = args self.kwargs = kwargs def tail_call_optimized(g): def func(*args, **kwargs): f = sys._getframe() if f.f_back and f.f_back.f_back and f.f_back.f_back.f_code == f.f_code: ...
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DataLoader(test_dset, shuffle=False, batch_size=64) Move the model to the CUDA device: model = BasicNet().to(device) Build a PyTorch optimizer: optimizer = optim.AdamW(model.parameters(), lr=1e-3) Before finally creating a simplistic training and evaluation loop that ...
Finally, RandRound when doing the color quantization in the DataLoader. For example, if the float level is 4.578, then there is a 57.8% chance to use 5, and (1-57.8%) chance to use 4. And we can allow both 4 and 5 in the prediction, but the loss will be higher if the predictio...
dataloader = pl.MpDeviceLoader(dataloader, device) Checkpoint Writing and Loading When a tensor is checkpointed from a XLA device and then loaded back from the checkpoint, it will be loaded back to the original device. Before checkpointing tensors in your model, you want to ensure that all ...
MpDeviceLoader(dataloader, device) Checkpoint Writing and Loading When a tensor is checkpointed from a XLA device and then loaded back from the checkpoint, it will be loaded back to the original device. Before checkpointing tensors in your model, you want to ensure that all of your tensors...
. The dataloader is wrapped in a container that will only grab the indices relevant to the current process in the sampler (or skip the batches for the other processes if you use an IterableDataset) and put the batches on the proper device. For this to work, Accelerate pr...