.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/loss/supervised/outlier_exposure.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_loss_supervised_outlier_exposure.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_loss_supervised_outlier_exposure.py:


Outlier Exposure
-------------------------

We train a model with  :class:`Outlier Exposure <pytorch_ood.loss.OutlierExposureLoss>` on the CIFAR10.

We can use a model pre-trained on the :math:`32 \times 32` resized version of the ImageNet as a foundation.
As outlier data, we use :class:`TinyImages300k <pytorch_ood.dataset.img.TinyImages300k>`, a cleaned version of the
TinyImages database, which contains random images scraped from the internet.

.. GENERATED FROM PYTHON SOURCE LINES 14-32

.. code-block:: Python
   :lineno-start: 14

    import torch
    import torchvision.transforms as tvt
    from torch.optim import Adam
    from torch.utils.data import DataLoader
    from torchvision.datasets import CIFAR10

    from pytorch_ood.dataset.img import Textures, TinyImages300k
    from pytorch_ood.detector import MaxSoftmax
    from pytorch_ood.loss import OutlierExposureLoss
    from pytorch_ood.model import WideResNet
    from pytorch_ood.utils import OODMetrics, ToUnknown

    torch.manual_seed(123)

    # maximum number of epochs and training iterations
    n_epochs = 10
    device = "cuda:0"


.. GENERATED FROM PYTHON SOURCE LINES 33-34

Setup preprocessing and data

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.. code-block:: Python
   :lineno-start: 34

    trans = tvt.Compose([tvt.Resize(size=(32, 32)), tvt.ToTensor()])

    # setup IN training data
    dataset_in_train = CIFAR10(root="data", train=True, download=True, transform=trans)

    # setup OOD training data, use ToUnknown() to mark labels as OOD
    # this way, outlier exposure can automatically decide if the training samples are IN or OOD
    dataset_out_train = TinyImages300k(
        root="data", download=True, transform=trans, target_transform=ToUnknown()
    )

    # setup IN test data
    dataset_in_test = CIFAR10(root="data", train=False, transform=trans)

    # setup OOD test data, use ToUnknown() to mark labels as OOD
    dataset_out_test = Textures(
        root="data", download=True, transform=trans, target_transform=ToUnknown()
    )

    # create data loaders
    train_loader = DataLoader(
        dataset_in_train + dataset_out_train, batch_size=64, shuffle=True
    )
    test_loader = DataLoader(dataset_in_test + dataset_out_test, batch_size=64)


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Create DNN, pretrained on the imagenet excluding cifar10 classes

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.. code-block:: Python
   :lineno-start: 61

    model = WideResNet(num_classes=1000, pretrained="imagenet32-nocifar")
    # we have to replace the final layer to account for the lower number of
    # classes in the CIFAR10 dataset
    model.fc = torch.nn.Linear(model.fc.in_features, 10)

    model.to(device)

    opti = Adam(model.parameters())
    criterion = OutlierExposureLoss(alpha=0.5)



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Define a function to test the model

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.. code-block:: Python
   :lineno-start: 74

    def test():
        softmax = MaxSoftmax(model)

        metrics_softmax = OODMetrics()
        model.eval()

        with torch.no_grad():
            for x, y in test_loader:
                metrics_softmax.update(softmax(x.to(device)), y)

        print(metrics_softmax.compute())
        model.train()



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Start training

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.. code-block:: Python
   :lineno-start: 90

    for epoch in range(n_epochs):
        print(f"Epoch {epoch}")
        for x, y in train_loader:
            logits = model(x.to(device))
            loss = criterion(logits, y.to(device))
            opti.zero_grad()
            loss.backward()
            opti.step()

        test()


.. _sphx_glr_download_auto_examples_loss_supervised_outlier_exposure.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: outlier_exposure.ipynb <outlier_exposure.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: outlier_exposure.py <outlier_exposure.py>`


.. only:: html

 .. rst-class:: sphx-glr-signature

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