.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/loss/supervised/cifar_vos.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_cifar_vos.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_loss_supervised_cifar_vos.py:


Virtual  Outlier Synthesizer Loss
-------------------------------------

We train a model with :class:`Virtual Outlier Synthesizer Loss<pytorch_ood.loss.vos.VirtualOutlierSynthesizingRegLoss>` on the CIFAR10.

We then use the :class:`WeightedEBO<pytorch_ood.detector.WeightedEBO>` OOD detector.

We can use a model pre-trained on the :math:`32 \times 32` resized version of the ImageNet as a foundation.

.. GENERATED FROM PYTHON SOURCE LINES 11-35

.. code-block:: Python
   :lineno-start: 12


    import numpy as np
    import torch
    from torch.optim.lr_scheduler import CosineAnnealingLR
    from torch.utils.data import DataLoader
    from torchvision.datasets import CIFAR10

    from pytorch_ood.dataset.img import Textures
    from pytorch_ood.detector import EnergyBased, WeightedEBO
    from pytorch_ood.loss import VirtualOutlierSynthesizingRegLoss
    from pytorch_ood.model import WideResNet
    from pytorch_ood.utils import OODMetrics, ToUnknown, fix_random_seed

    device = "cuda:0"
    batch_size = 128
    num_epochs = 10
    lr = 0.1
    num_classes = 10

    fix_random_seed(12345)
    g = torch.Generator()
    g.manual_seed(0)



.. GENERATED FROM PYTHON SOURCE LINES 36-37

Setup datasets, train on cifar.

.. GENERATED FROM PYTHON SOURCE LINES 37-59

.. code-block:: Python
   :lineno-start: 37

    trans = WideResNet.transform_for("cifar10-pt")

    dataset = CIFAR10(root="data", train=True, transform=trans, download=True)

    # setup ID 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()
    )


    loader = DataLoader(
        dataset,
        batch_size=batch_size,
        shuffle=True,
        num_workers=10,
        worker_init_fn=fix_random_seed,
        generator=g,
    )


.. GENERATED FROM PYTHON SOURCE LINES 60-61

Setup model

.. GENERATED FROM PYTHON SOURCE LINES 61-63

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

    model = WideResNet(num_classes=10, pretrained="cifar10-pt").to(device)


.. GENERATED FROM PYTHON SOURCE LINES 64-65

Create neural network functions (layers)

.. GENERATED FROM PYTHON SOURCE LINES 65-82

.. code-block:: Python
   :lineno-start: 65

    phi = torch.nn.Linear(1, 2).to(device)
    weights_energy = torch.nn.Linear(num_classes, 1).to(device)
    torch.nn.init.uniform_(weights_energy.weight)

    criterion = VirtualOutlierSynthesizingRegLoss(
        phi,
        weights_energy,
        device=device,
        num_classes=num_classes,
        num_input_last_layer=128,
        fc=model.fc,
        sample_number=10000,
        select=1,
        sample_from=1000,
        alpha=0.1,
    )


.. GENERATED FROM PYTHON SOURCE LINES 83-84

Train model for some epochs

.. GENERATED FROM PYTHON SOURCE LINES 84-117

.. code-block:: Python
   :lineno-start: 84

    optimizer = torch.optim.SGD(
        list(model.parameters()) + list(phi.parameters()) + list(weights_energy.parameters()),
        lr=lr,
        momentum=0.9,
        weight_decay=5e-4,
    )


    # setup scheduler for optimizer (recommended)
    scheduler = CosineAnnealingLR(
        optimizer,
        T_max=num_epochs * len(loader),
    )
    loss_ema = 0

    for epoch in range(num_epochs):
        for n, (x, y) in enumerate(loader):
            optimizer.zero_grad()
            y, x = y.to(device), x.to(device)

            features = model.features(x)
            y_hat = model.fc(features)
            loss = criterion(y_hat, features, y)

            loss.backward()
            optimizer.step()
            scheduler.step()

            loss_ema = 0.8 * loss_ema + 0.2 * loss.item()

            if n % 10 == 0:
                print(f"Epoch {epoch:03d} [{n:05d}/{len(loader):05d}] \t Loss: {loss_ema:02.2f}")


.. GENERATED FROM PYTHON SOURCE LINES 118-119

Evaluate

.. GENERATED FROM PYTHON SOURCE LINES 119-141

.. code-block:: Python
   :lineno-start: 119

    print("Evaluating")
    model.eval()
    test_loader = DataLoader(dataset_in_test + dataset_out_test, batch_size=64)
    detector_weightedEBO = WeightedEBO(model, weights_energy)
    detector_energyBased = EnergyBased(model)
    metrics_weightedEBO = OODMetrics()
    metrics_energyBased = OODMetrics()

    with torch.no_grad():
        for n, (x, y) in enumerate(test_loader):
            y, x = y.to(device), x.to(device)
            y_hat = model(x)
            o = detector_weightedEBO.predict_features(y_hat)
            o1 = detector_energyBased.predict_features(y_hat)

            metrics_weightedEBO.update(o, y)
            metrics_energyBased.update(o1, y)
            if n % 10 == 0:
                print(f"Epoch {epoch:03d} [{n:05d}/{len(test_loader):05d}] ")

    print(f"WeightedEBO: {metrics_weightedEBO.compute()}")
    print(f"EnergyBased: {metrics_energyBased.compute()}")

.. GENERATED FROM PYTHON SOURCE LINES 142-145

Output:
WeightedEBO: {'AUROC': 0.9192541837692261, 'AUPR-IN': 0.8389347195625305, 'AUPR-OUT': 0.954131007194519, 'FPR95TPR': 0.2897000014781952}
EnergyBased: {'AUROC': 0.9227883815765381, 'AUPR-IN': 0.8493221998214722, 'AUPR-OUT': 0.956129789352417, 'FPR95TPR': 0.2799000144004822}


.. _sphx_glr_download_auto_examples_loss_supervised_cifar_vos.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: cifar_vos.zip <cifar_vos.zip>`


.. only:: html

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

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_