164 lines
6.1 KiB
Python
164 lines
6.1 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Any, Dict, Optional
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import torch
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from sklearn.datasets import make_classification, make_regression
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from sklearn.model_selection import train_test_split
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from torch.utils.data import DataLoader, random_split
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from torch.utils.data.distributed import DistributedSampler
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from pytorch_lightning.core.datamodule import LightningDataModule
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from tests.helpers.datasets import MNIST, SklearnDataset, TrialMNIST
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class TrialMNISTDataModule(LightningDataModule):
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def __init__(self, data_dir: str = "./"):
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super().__init__()
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self.data_dir = data_dir
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self.non_picklable = None
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self.checkpoint_state: Optional[str] = None
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def prepare_data(self):
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TrialMNIST(self.data_dir, train=True, download=True)
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TrialMNIST(self.data_dir, train=False, download=True)
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def setup(self, stage: Optional[str] = None):
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if stage == "fit" or stage is None:
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mnist_full = TrialMNIST(root=self.data_dir, train=True, num_samples=64, download=True)
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self.mnist_train, self.mnist_val = random_split(mnist_full, [128, 64])
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self.dims = self.mnist_train[0][0].shape
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if stage == "test" or stage is None:
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self.mnist_test = TrialMNIST(root=self.data_dir, train=False, num_samples=64, download=True)
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self.dims = getattr(self, "dims", self.mnist_test[0][0].shape)
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self.non_picklable = lambda x: x**2
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def train_dataloader(self):
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return DataLoader(self.mnist_train, batch_size=32)
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def val_dataloader(self):
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return DataLoader(self.mnist_val, batch_size=32)
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def test_dataloader(self):
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return DataLoader(self.mnist_test, batch_size=32)
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def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
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checkpoint[self.__class__.__name__] = self.__class__.__name__
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def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
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self.checkpoint_state = checkpoint.get(self.__class__.__name__)
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class MNISTDataModule(LightningDataModule):
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def __init__(self, data_dir: str = "./", batch_size: int = 32, dist_sampler: bool = False) -> None:
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super().__init__()
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self.dist_sampler = dist_sampler
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self.data_dir = data_dir
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self.batch_size = batch_size
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# self.dims is returned when you call dm.size()
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# Setting default dims here because we know them.
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# Could optionally be assigned dynamically in dm.setup()
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self.dims = (1, 28, 28)
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def prepare_data(self):
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# download only
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MNIST(self.data_dir, train=True, download=True, normalize=(0.1307, 0.3081))
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MNIST(self.data_dir, train=False, download=True, normalize=(0.1307, 0.3081))
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def setup(self, stage: Optional[str] = None):
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# Assign train/val datasets for use in dataloaders
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# TODO: need to split using random_split once updated to torch >= 1.6
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if stage == "fit" or stage is None:
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self.mnist_train = MNIST(self.data_dir, train=True, normalize=(0.1307, 0.3081))
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# Assign test dataset for use in dataloader(s)
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if stage == "test" or stage is None:
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self.mnist_test = MNIST(self.data_dir, train=False, normalize=(0.1307, 0.3081))
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def train_dataloader(self):
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dist_sampler = None
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if self.dist_sampler:
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dist_sampler = DistributedSampler(self.mnist_train, shuffle=False)
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return DataLoader(
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self.mnist_train,
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batch_size=self.batch_size,
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sampler=dist_sampler,
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shuffle=False,
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)
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def test_dataloader(self):
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return DataLoader(self.mnist_test, batch_size=self.batch_size, shuffle=False)
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class SklearnDataModule(LightningDataModule):
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def __init__(self, sklearn_dataset, x_type, y_type, batch_size: int = 10):
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super().__init__()
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self.batch_size = batch_size
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self._x, self._y = sklearn_dataset
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self._split_data()
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self._x_type = x_type
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self._y_type = y_type
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def _split_data(self):
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self.x_train, self.x_test, self.y_train, self.y_test = \
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train_test_split(self._x, self._y, test_size=0.20, random_state=42)
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self.x_train, self.x_valid, self.y_train, self.y_valid = \
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train_test_split(self.x_train, self.y_train, test_size=0.40, random_state=42)
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def train_dataloader(self):
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return DataLoader(
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SklearnDataset(self.x_train, self.y_train, self._x_type, self._y_type), batch_size=self.batch_size
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)
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def val_dataloader(self):
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return DataLoader(
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SklearnDataset(self.x_valid, self.y_valid, self._x_type, self._y_type), batch_size=self.batch_size
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)
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def test_dataloader(self):
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return DataLoader(
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SklearnDataset(self.x_test, self.y_test, self._x_type, self._y_type), batch_size=self.batch_size
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)
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@property
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def sample(self):
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return torch.tensor([self._x[0]], dtype=self._x_type)
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class ClassifDataModule(SklearnDataModule):
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def __init__(self, num_features=32, length=800, num_classes=3, batch_size=10):
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data = make_classification(
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n_samples=length, n_features=num_features, n_classes=num_classes, n_clusters_per_class=1, random_state=42
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)
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super().__init__(data, x_type=torch.float32, y_type=torch.long, batch_size=batch_size)
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class RegressDataModule(SklearnDataModule):
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def __init__(self, num_features=16, length=800, batch_size=10):
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x, y = make_regression(n_samples=length, n_features=num_features, random_state=42)
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y = [[v] for v in y]
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super().__init__((x, y), x_type=torch.float32, y_type=torch.float32, batch_size=batch_size)
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