class MNISTDataModule(pl.LightningDataModule): def __init__(self, data_dir: str = "./minist/", batch_size: int = 32, num_workers: int =4): super().__init__() self.data_dir = data_dir self.batch_size = batch_size self.num_workers = num_workers def setup(self, stage = Non...
class MNISTDataModule(LightningDataModule): def __init__(self,root_dir,val_size,num_workers,batch_size): super(MNISTDataModule, self).__init__() self.save_hyperparameters() def prepare_data(self): """ download data once """ MNIST(self.hparams.root_dir, train=True, download=True) MNI...
class LitMNISTDreamer(LightningModule): def forward(self, z): imgs = self.decoder(z) return imgs def predict_step(self, batch, batch_idx: int , dataloader_idx: int = None): return self(batch) model = LitMNISTDreamer() trainer.predict(model, datamodule) 如何划分forward , training_step,...
API页面:https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html%23lightningmodule-api 一个Pytorch-Lighting 模型必须含有的部件是: init: 初始化,包括模型和系统的定义。 training_step(self, batch, batch_idx): 即每个batch的处理函数。 参数:...
被抽象为LightningModule类。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 class LitClassifier(pl.LightningModule): def __init__(self, hidden_dim=128, learning_rate=1e-3): super().__init__() self.save_hyperparameters() self.l1 = torch.nn.Linear(28 * 28, self.hparams.hidden_dim...
先如下定义如下的LightningModule和main函数。 class CoolSystem(pl.LightningModule): def __init__(self, hparams): super(CoolSystem, self).__init__() self.params = hparams self.data_dir = self.params.data_dir self.num_classes = self.params.num_classes ...
pip install pytorch-lightning 1. 或者通过conda安装: conda install pytorch-lightning -c conda-forge 1. 3. Lightning的设计思想 Lightning将大部分AI相关代码分为三个部分: 研究代码,主要是模型的结构、训练等部分。被抽象为LightningModule类。 工程代码,这部分代码重复性强,比如16位精度,分布式训练。被抽象为Tr...
接下来,我们需要定义一个DataModule来处理数据集的加载和预处理。 fromtorch.utils.dataimportDataLoader, random_splitfromtorchvision.datasetsimportMNISTfromtorchvisionimporttransformsclassMNISTDataModule(pl.LightningDataModule):def__init__(self, data_dir='./', batch_size=32):super().__init__()self.data...
class ProteinDataModule(pl.LightningDataModule): def __init__( self, *, num_classes=10, valid_pct=0.1, resize_to=(384, 384), batch_size=32, num_workers=0, pin_memory=False, shuffle_validation=False, ): super().__init__()
🐛 Bug The Trainer expects the LightningModule to have self.batch_size (see scale_batch_size() in training_tricks.py). However, if one is using the new LightningDataModule, that should be the class with self.batch_size defined. To Reprodu...