在自动优化中,training_step必须返回一个tensor或者dict或者None(跳过),对于简单的使用,在training_step可以return一个tensor会作为Loss回传,也可以return一个字典,其中必须包括key"loss",字典中的"loss"会提取出来作为Loss回传,具体过程主要包含在lightning\pytorch\loops\automatic.py中的_AutomaticOptimization()类。 cl...
* 如果你的`training_step`真的不返回一个loss,那么就会跳过automatic optimization,仅适用于需要手动optimize的case。 * 对于`validation_step`, `test_step`, 和`predict_step`,它们还接受一个额外的`dataloader_idx`,以供记录在多个DataLoader的场景中具体该batch来自哪个DataLoader。 * 上面三个hooks可以选择return...
from pytorch_lightning.metricsimportMetricclassMyAccuracy(Metric):def__init__(self,dist_sync_on_step=False):super().__init__(dist_sync_on_step=dist_sync_on_step)self.add_state("correct",default=torch.tensor(0),dist_reduce_fx="sum")self.add_state("total",default=torch.tensor(0),dist_...
from torch.utils.data import DataLoader, random_split import pytorch_lightning as pl class MyExampleModel(pl.LightningModule): def __init__(self, args): super().__init__() dataset = MNIST(os.getcwd(), download=True, transform=transforms.ToTensor()) train_dataset, val_dataset, test_dataset...
import pytorch_lightning as pl 1. 2. 3. 4. 5. 6. 7. 8. Step 2: Define a LightningModule (nn.Module subclass) class LitAutoEncoder(pl.LightningModule): def __init__(self): super().__init__() self.encoder = nn.Sequential(nn.Linear(28 * 28, 128), nn.ReLU(), nn.Linear(128...
trainer *=* Trainer(automatic_optimization*=False*) 现在训练循环已经由用户自己掌握。 def training_step(self, batch, batch_idx, opt_idx):(opt_a, opt_b, opt_c) = self.optimizers()loss_a = self.generator(batch[0])# use this instead of loss.backward so we can automate half# precision,...
The LightningModule is an extension of the nn.Module class. It combines the training, validation, testing, prediction, and optimization steps of the PyTorch workflow into a single interface without loops. When you start using LightningModule, the PyTorch code isn't abstracted; it’s organized ...
pytorch_lightning.metrics 是一种 Metrics API,旨在在 PyTorch 和 PyTorch Lightning 中轻松地进行度量指标的开发和使用。更新后的 API 提供了一种内置方法,可针对每个步骤跨多个 GPU(进程)计算指标,同时存储统计信息。这可以让用户在一个阶段结束时计算指标,而无需担心任何与分布式后端相关的复杂度。
Bug description Hello, I encountered a bug when training with automatic_optimization = False and two optimizers. In summary: the global_step attribute of the trainer and the lightning module is tracking the total number of calls to optim...
[rank1]: File "/home/jyc/miniconda3/envs/icl/lib/python3.12/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 250, in advance [rank1]: batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs) ...