def setup(self): mnist_full = TrialMNIST(root=self.data_dir, train=True, num_samples=64, download=True) self.mnist_train, self.mnist_val = random_split(mnist_full, [128, 64]) self.dims = tuple(self.mnist_train[0][0].shape) self.mnist_test = TrialMNIST(root=self.data_dir, train=False, num_samples=32, download=True)
def setup(self, stage: Optional[str] = None): mnist_full = TrialMNIST( root=self.data_dir, train=True, num_samples=64, download=True ) self.mnist_train, self.mnist_val = random_split(mnist_full, [128, 64]) self.dims = self.mnist_train[0][0].shape
def setup(self, stage: Optional[str] = None): if stage == "fit" or stage is None: mnist_full = TrialMNIST( root=self.data_dir, train=True, num_samples=64, download=True ) self.mnist_train, self.mnist_val = random_split(mnist_full, [128, 64]) self.dims = self.mnist_train[0][0].shape if stage == "test" or stage is None: self.mnist_test = TrialMNIST( root=self.data_dir, train=False, num_samples=64, download=True ) self.dims = getattr(self, "dims", self.mnist_test[0][0].shape) self.non_picklable = lambda x: x ** 2
def dataloader(self, train): dataset = TrialMNIST(root=self.hparams.data_root, train=train, download=True) loader = DataLoader(dataset=dataset, batch_size=self.hparams.batch_size, shuffle=True) return loader
def dataloader(self, train): dataset = TrialMNIST(root=self.data_root, train=train, download=True) loader = DataLoader( dataset=dataset, batch_size=self.batch_size, num_workers=3, shuffle=train, ) return loader
def dataloader(self, train): dataset = TrialMNIST(root=self.hparams.data_root, train=train, download=True) loader = DataLoader( dataset=dataset, batch_size=self.hparams.batch_size, # test and valid shall not be shuffled shuffle=train, ) return loader
def dataloader(self, train: bool, num_samples: int = 100): dataset = TrialMNIST(root=self.data_root, train=train, num_samples=num_samples, download=True) loader = DataLoader( dataset=dataset, batch_size=self.batch_size, num_workers=0, shuffle=train, ) return loader
def _dataloader(self, train): # init data generators dataset = TrialMNIST(root=self.hparams.data_root, train=train, download=True) # when using multi-node we need to add the datasampler batch_size = self.hparams.batch_size loader = DataLoader( dataset=dataset, batch_size=batch_size, shuffle=True ) return loader
def prepare_data(self): TrialMNIST(root=self.data_root, train=True, download=True)
def prepare_data(self): TrialMNIST(self.data_dir, train=True, download=True) TrialMNIST(self.data_dir, train=False, download=True)
def train_dataloader(self): return DataLoader(TrialMNIST(train=True, download=True), batch_size=16)
from pytorch_lightning import Trainer, seed_everything from pytorch_lightning.accelerators import TPUAccelerator from pytorch_lightning.callbacks import EarlyStopping from pytorch_lightning.utilities import TPU_AVAILABLE from pytorch_lightning.utilities.exceptions import MisconfigurationException from tests.base import EvalModelTemplate from tests.base.datasets import TrialMNIST from tests.base.develop_utils import pl_multi_process_test if TPU_AVAILABLE: import torch_xla import torch_xla.distributed.xla_multiprocessing as xmp SERIAL_EXEC = xmp.MpSerialExecutor() _LARGER_DATASET = TrialMNIST(download=True, num_samples=2000, digits=(0, 1, 2, 5, 8)) # 8 cores needs a big dataset def _serial_train_loader(): return DataLoader(_LARGER_DATASET, batch_size=32) @pytest.mark.skipif(not TPU_AVAILABLE, reason="test requires TPU machine") @pl_multi_process_test def test_model_tpu_cores_1(tmpdir): """Make sure model trains on TPU.""" trainer_options = dict( default_root_dir=tmpdir, progress_bar_refresh_rate=0,
def train_dataloader(self): return DataLoader(TrialMNIST(train=True, download=True, num_samples=500, digits=list(range(5))), batch_size=128)
def long_train_loader(): dataset = DataLoader(TrialMNIST(download=True, num_samples=15000, digits=(0, 1, 2, 5, 8)), batch_size=32) return dataset
def test_dataloader(self): return DataLoader(TrialMNIST(train=False, num_samples=50), batch_size=16)
def train_dataloader(self): return DataLoader(TrialMNIST(train=True, num_samples=100), batch_size=16)