File size: 2,525 Bytes
0c717d3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | import lightning as L
from pathlib import Path
from typing import Union
import splitfolders
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.datasets import ImageFolder
from torchvision.datasets.utils import extract_archive
class ImageNetDataModule(L.LightningDataModule):
def __init__(self, dl_path: Union[str, Path] = "data", num_workers: int = 0, batch_size: int = 8):
super().__init__()
self._dl_path = dl_path
self._num_workers = num_workers
self._batch_size = batch_size
# def prepare_data(self):
# extract_archive(
# from_path="dog-breed-image-dataset.zip",
# to_path=self._dl_path,
# remove_finished=False
# )
# splitfolders.ratio(
# Path(self._dl_path).joinpath('dataset'),
# output="data/dogs_filtered",
# ratio=(.8, .1, .1)
# )
@property
def data_path(self):
return Path(self._dl_path).joinpath("imagenet-dataset")
@property
def normalize_transform(self):
return transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
@property
def train_transform(self):
return transforms.Compose([
transforms.RandomResizedCrop(224, interpolation=transforms.InterpolationMode.BILINEAR, antialias=True),
transforms.RandomHorizontalFlip(0.5),
transforms.ToTensor(),
self.normalize_transform,
])
@property
def valid_transform(self):
return transforms.Compose([
transforms.Resize((224, 224)),
transforms.Resize(size=256, antialias=True),
transforms.CenterCrop(224),
transforms.ToTensor(),
self.normalize_transform
])
def create_dataset(self, root, transform):
return ImageFolder(root=root, transform=transform)
def __dataloader(self, train: bool):
if train:
dataset = self.create_dataset(self.data_path.joinpath("train"), self.train_transform)
else:
dataset = self.create_dataset(self.data_path.joinpath("val"), self.valid_transform)
return DataLoader(
dataset=dataset,
batch_size=self._batch_size,
num_workers=self._num_workers,
shuffle=train
)
def train_dataloader(self):
return self.__dataloader(train=True)
def val_dataloader(self):
return self.__dataloader(train=False) |