This document lists the data APIs in Gluon:
.. autosummary:: :nosignatures: mxnet.gluon.data mxnet.gluon.data.vision
The Gluon Data API, defined in the gluon.data package, provides useful dataset loading and processing tools, as well as common public datasets.
In the rest of this document, we list routines provided by the gluon.data package.
.. currentmodule:: mxnet.gluon.data
.. autosummary:: :nosignatures: Dataset ArrayDataset RecordFileDataset
.. autosummary:: :nosignatures: Sampler SequentialSampler RandomSampler BatchSampler
.. autosummary:: :nosignatures: DataLoader
.. currentmodule:: mxnet.gluon.data.vision.datasets
.. autosummary:: :nosignatures: MNIST FashionMNIST CIFAR10 CIFAR100 ImageRecordDataset ImageFolderDataset
.. currentmodule:: mxnet.gluon.data.vision.transforms
Transforms can be used to augment input data during training. You can compose multiple transforms sequentially (taking note of which functions should be applied before and after ToTensor).
from mxnet.gluon.data.vision import MNIST, transforms from mxnet import gluon transform = transforms.Compose([ transforms.Resize(300), transforms.RandomResizedCrop(224), transforms.RandomBrightness(0.1), transforms.ToTensor(), transforms.Normalize(0, 1)]) data = MNIST(train=True).transform_first(transform) data_loader = gluon.data.DataLoader(data, batch_size=32, num_workers=1) for data, label in data_loader: # do something with data and label
.. autosummary:: :nosignatures: Compose Cast ToTensor Normalize RandomResizedCrop CenterCrop Resize RandomFlipLeftRight RandomFlipTopBottom RandomBrightness RandomContrast RandomSaturation RandomHue RandomColorJitter RandomLighting
.. automodule:: mxnet.gluon.data :members: :imported-members: .. automodule:: mxnet.gluon.data.vision :members: .. automodule:: mxnet.gluon.data.vision.datasets :members: .. automodule:: mxnet.gluon.data.vision.transforms :members: