144 lines
4.7 KiB
Python
144 lines
4.7 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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import platform
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from typing import Optional
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from warnings import warn
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from torch.utils.data import DataLoader, random_split
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from pl_examples import _DATASETS_PATH, _TORCHVISION_AVAILABLE
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from pytorch_lightning import LightningDataModule
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if _TORCHVISION_AVAILABLE:
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from torchvision import transforms as transform_lib
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from torchvision.datasets import MNIST
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else:
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from tests.helpers.datasets import MNIST
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class MNISTDataModule(LightningDataModule):
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"""
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Standard MNIST, train, val, test splits and transforms
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>>> MNISTDataModule() # doctest: +ELLIPSIS
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<...mnist_datamodule.MNISTDataModule object at ...>
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"""
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name = "mnist"
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def __init__(
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self,
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data_dir: str = _DATASETS_PATH,
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val_split: int = 5000,
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num_workers: int = 16,
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normalize: bool = False,
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seed: int = 42,
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batch_size: int = 32,
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*args,
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**kwargs,
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):
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"""
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Args:
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data_dir: where to save/load the data
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val_split: how many of the training images to use for the validation split
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num_workers: how many workers to use for loading data
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normalize: If true applies image normalize
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"""
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super().__init__(*args, **kwargs)
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if num_workers and platform.system() == "Windows":
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# see: https://stackoverflow.com/a/59680818
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warn(
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f"You have requested num_workers={num_workers} on Windows,"
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" but currently recommended is 0, so we set it for you"
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)
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num_workers = 0
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self.dims = (1, 28, 28)
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self.data_dir = data_dir
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self.val_split = val_split
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self.num_workers = num_workers
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self.normalize = normalize
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self.seed = seed
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self.batch_size = batch_size
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self.dataset_train = ...
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self.dataset_val = ...
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self.test_transforms = self.default_transforms
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@property
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def num_classes(self):
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return 10
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def prepare_data(self):
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"""Saves MNIST files to `data_dir`"""
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MNIST(self.data_dir, train=True, download=True)
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MNIST(self.data_dir, train=False, download=True)
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def setup(self, stage: Optional[str] = None):
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"""Split the train and valid dataset"""
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extra = dict(transform=self.default_transforms) if self.default_transforms else {}
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dataset = MNIST(self.data_dir, train=True, download=False, **extra)
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train_length = len(dataset)
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self.dataset_train, self.dataset_val = random_split(dataset, [train_length - self.val_split, self.val_split])
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def train_dataloader(self):
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"""MNIST train set removes a subset to use for validation"""
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loader = DataLoader(
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self.dataset_train,
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batch_size=self.batch_size,
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shuffle=True,
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num_workers=self.num_workers,
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drop_last=True,
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pin_memory=True,
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)
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return loader
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def val_dataloader(self):
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"""MNIST val set uses a subset of the training set for validation"""
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loader = DataLoader(
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self.dataset_val,
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batch_size=self.batch_size,
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shuffle=False,
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num_workers=self.num_workers,
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drop_last=True,
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pin_memory=True,
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)
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return loader
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def test_dataloader(self):
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"""MNIST test set uses the test split"""
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extra = dict(transform=self.test_transforms) if self.test_transforms else {}
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dataset = MNIST(self.data_dir, train=False, download=False, **extra)
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loader = DataLoader(
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dataset,
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batch_size=self.batch_size,
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shuffle=False,
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num_workers=self.num_workers,
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drop_last=True,
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pin_memory=True,
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)
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return loader
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@property
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def default_transforms(self):
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if not _TORCHVISION_AVAILABLE:
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return None
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if self.normalize:
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mnist_transforms = transform_lib.Compose([
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transform_lib.ToTensor(), transform_lib.Normalize(mean=(0.5, ), std=(0.5, ))
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])
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else:
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mnist_transforms = transform_lib.ToTensor()
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return mnist_transforms
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