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

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

.. _sphx_glr_auto_examples_loss_unsupervised_cac.py:


Class Anchor Clustering
-------------------------

:class:`Class Anchor Clustering <pytorch_ood.loss.CACLoss>` (CAC) can be seen as a multi-class generalization of Deep
One-Class Learning, where there are
several centers :math:`\{\mu_1, \mu_2, ..., \mu_y\}` in the output space of the model, one for each class.
During training, the representation :math:`f_{\theta}(x)` from class :math:`y` is drawn
towards the corresponding center :math:`\mu_y`.

Here, we train the model for 10 epochs on the CIFAR10 dataset, using a backbone pre-trained on the
:math:`32 \times 32` resized version of the ImageNet as a foundation.

.. GENERATED FROM PYTHON SOURCE LINES 14-50

.. code-block:: Python
   :lineno-start: 15


    import torch
    from torch.optim import Adam
    from torch.optim.lr_scheduler import CosineAnnealingLR
    from torch.utils.data import DataLoader
    from torchmetrics import Accuracy
    from torchvision.datasets import CIFAR10
    from tqdm import tqdm

    from pytorch_ood.dataset.img import Textures
    from pytorch_ood.loss import CACLoss
    from pytorch_ood.model import WideResNet
    from pytorch_ood.utils import OODMetrics, ToUnknown, fix_random_seed, is_known

    fix_random_seed(123)

    n_epochs = 10
    device = "cuda:0"

    trans = WideResNet.transform_for("imagenet32-nocifar")

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

    # 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, batch_size=64, shuffle=True, num_workers=16)
    test_loader = DataLoader(dataset_in_test + dataset_out_test, batch_size=64, num_workers=16)


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Create DNN, pretrained on the imagenet excluding cifar10 classes.
We have to replace the final layer to match the number of classes.

.. GENERATED FROM PYTHON SOURCE LINES 53-61

.. code-block:: Python
   :lineno-start: 53

    model = WideResNet(num_classes=1000, pretrained="imagenet32-nocifar")
    model.fc = torch.nn.Linear(model.fc.in_features, 10)
    model.to(device)

    opti = Adam(model.parameters())
    criterion = CACLoss(n_classes=10, magnitude=5, alpha=2).to(device)
    scheduler = CosineAnnealingLR(opti, T_max=n_epochs * len(train_loader))


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Define a function that evaluates the model

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



    def test():
        metrics = OODMetrics()
        acc = Accuracy(num_classes=10, task="multiclass")

        model.eval()

        with torch.no_grad(), tqdm(test_loader, desc="Testing") as bar:
            for x, y in bar:
                # calculate embeddings
                z = model(x.to(device))
                # calculate the distance of each embedding to each center
                distances = criterion.distance(z).cpu()
                # the CAC Loss proposes its own method for score calculation.
                # We could, however, also use the minimum distance.
                metrics.update(CACLoss.score(distances), y)
                known = is_known(y)
                if known.any():
                    acc.update(distances[known].min(dim=1).indices, y[known])

        print(metrics.compute())
        print(f"Accuracy: {acc.compute().item():.2%}")
        model.train()



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Start training

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.. code-block:: Python
   :lineno-start: 92


    for epoch in range(n_epochs):
        loss_ema = 0

        with tqdm(train_loader, desc=f"Epoch {epoch}") as bar:
            for x, y in bar:
                # calculate embeddings
                z = model(x.to(device))
                # calculate the distance of each embedding to each center
                distances = criterion.distance(z)
                # calculate CAC loss, based on distances to centers
                loss = criterion(distances, y.cuda())
                opti.zero_grad()
                loss.backward()
                opti.step()
                scheduler.step()

                loss_ema = loss.item() if not loss_ema else 0.99 * loss_ema + 0.01 * loss.item()
                bar.set_postfix_str(f"loss: {loss_ema:.3f} lr: {scheduler.get_last_lr()[0]:.6f}")

            test()


.. GENERATED FROM PYTHON SOURCE LINES 114-117

{'AUROC': 0.8958120346069336, 'AUPR-IN': 0.8456918001174927, 'AUPR-OUT': 0.8196930885314941, 'FPR95TPR': 0.5187000036239624}

Accuracy: 93.80%


.. _sphx_glr_download_auto_examples_loss_unsupervised_cac.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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