data和modle两个文件夹中放入__init__.py文件,做成包。这样方便导入。两个init文件分别是: from .data_interface import DInterface from .model_interface import MInterface 在data_interface中建立一个class DInterface(pl.LightningDataModule):用作所有数据集文件的接口。__init__()函数中import相应Dataset类,se...
data和model两个文件夹中放入__init__.py文件,做成包。这样方便导入。两个init文件分别是: from .data_interface import DInterface from .model_interface import MInterface 在data_interface中建立一个class DInterface(pl.LightningDataModule):用作所有数据集文件的接口。__init__()函数中import相应Dataset类,se...
Trainer.fit(model, train_dataloader=None, val_dataloaders=None, datamodule=None):输入第一个量一定是model,然后可以跟一个LigntningDataModule或一个普通的Train DataLoader。如果定义了Val step,也要有Val DataLoader。 参数: datamodule ([Optional] [LightningDataModule]...
# main.pyfromlightning.pytorch.cliimportLightningCLIfromlightning.pytorch.demos.boring_classesimportDemoModel,BoringDataModuleclassModel1(DemoModel):defconfigure_optimizers(self):print("⚡","using Model1","⚡")returnsuper().configure_optimizers()classModel2(DemoModel):defconfigure_optimizers(self):pri...
Handle data loading, preprocessing, and batching simultaneously. Support multi-threading to maximize CPU utilization (num_workers). Minimize training bottlenecks. Shuffle the data after every epoch (shuffle). Be highly efficient for large datasets. Whether you use classic Torch or the new Lightning, ...
from lightning.fabric.utilities import LightningEnum # noqa: F401 from lightning.fabric.utilities import move_data_to_device # noqa: F401 from lightning.fabric.utilities import suggested_max_num_workers # noqa: F401 from lightning.pytorch.utilities.combined_loader import CombinedLoader # noqa: F401 ...
在data_interface中建立一个class DInterface(pl.LightningDataModule):用作所有数据集文件的接口。__init__()函数中import相应Dataset类,setup()进行实例化,并老老实实加入所需要的的train_dataloader, val_dataloader, ...
fromlightning_fabric.plugins.environmentsimportSLURMEnvironment fromlightning_fabric.utilitiesimportmove_data_to_device fromlightning_fabric.utilities.apply_funcimportconvert_tensors_to_scalars frompytorch_lightning.loggersimportLogger,TensorBoardLogger frompytorch_lightning.loggersimportCSVLogger,Logger,TensorBoardLogge...
Automatically move data to correct device during inference class LitModel(LightningModule): @auto_move_data def forward(self, x): return xmodel = LitModel() x = torch.rand(2, 3) model = model.cuda(2)# this works! model(x) many more speed improvements including single-TPU speed-ups (...
to(device, non_blocking=True) class DeviceDataLoader(): """Wrap a dataloader to move data to a device""" def __init__(self, dl, device): self.dl = dl self.device = device def __iter__(self): """Yield a batch of data after moving it to device""" for b in self.dl: ...