.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/benchmarks/manual/cifar10_baseline.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_benchmarks_manual_cifar10_baseline.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_benchmarks_manual_cifar10_baseline.py:


CIFAR 10
==============================

Example benchmark code for CIFAR10

+------------------+-------+-------+---------+----------+----------+
| Detector         | AUROC | AUTC  | AUPR-IN | AUPR-OUT | FPR95TPR |
+==================+=======+=======+=========+==========+==========+
| Gram             | 69.37 | 46.01 | 58.02   | 77.49    | 75.03    |
+------------------+-------+-------+---------+----------+----------+
| KLMatching       | 88.48 | 39.83 | 72.29   | 91.33    | 57.84    |
+------------------+-------+-------+---------+----------+----------+
| SHE              | 90.08 | 39.69 | 69.17   | 92.92    | 38.48    |
+------------------+-------+-------+---------+----------+----------+
| MSP              | 91.41 | 37.07 | 86.36   | 92.42    | 29.93    |
+------------------+-------+-------+---------+----------+----------+
| Entropy          | 92.03 | 35.90 | 86.70   | 93.47    | 29.75    |
+------------------+-------+-------+---------+----------+----------+
| Mahalanobis      | 92.14 | 42.76 | 86.39   | 94.36    | 28.25    |
+------------------+-------+-------+---------+----------+----------+
| ODIN             | 92.14 | 47.06 | 84.98   | 94.46    | 34.43    |
+------------------+-------+-------+---------+----------+----------+
| ViM              | 92.32 | 40.22 | 85.76   | 94.93    | 29.49    |
+------------------+-------+-------+---------+----------+----------+
| Mahalanobis+ODIN | 92.60 | 42.76 | 86.81   | 95.08    | 27.11    |
+------------------+-------+-------+---------+----------+----------+
| DICE             | 92.80 | 35.83 | 86.68   | 94.20    | 32.35    |
+------------------+-------+-------+---------+----------+----------+
| MaxLogit         | 93.05 | 35.84 | 87.01   | 94.40    | 31.31    |
+------------------+-------+-------+---------+----------+----------+
| EnergyBased      | 93.11 | 35.45 | 87.09   | 94.46    | 31.14    |
+------------------+-------+-------+---------+----------+----------+
| MultiMahalanobis | 93.43 | 44.60 | 86.70   | 96.48    | 22.95    |
+------------------+-------+-------+---------+----------+----------+
| RMD              | 93.46 | 32.09 | 87.73   | 95.08    | 26.99    |
+------------------+-------+-------+---------+----------+----------+

.. GENERATED FROM PYTHON SOURCE LINES 43-80

.. code-block:: Python
   :lineno-start: 44


    import pandas as pd  # additional dependency, used here for convenience
    import torch
    from torch import nn
    from torch.utils.data import DataLoader
    from torchvision.datasets import CIFAR10, CIFAR100, MNIST, FashionMNIST

    from pytorch_ood.dataset.img import (
        LSUNCrop,
        LSUNResize,
        Textures,
        TinyImageNetCrop,
        TinyImageNetResize,
        Places365,
    )
    from pytorch_ood.detector import (
        ODIN,
        EnergyBased,
        Entropy,
        KLMatching,
        Mahalanobis,
        MaxLogit,
        MaxSoftmax,
        ViM,
        RMD,
        DICE,
        SHE,
        Gram,
        MultiMahalanobis,
    )
    from pytorch_ood.model import WideResNet
    from pytorch_ood.utils import OODMetrics, ToUnknown, fix_random_seed

    device = "cuda:0"

    fix_random_seed(123)


.. GENERATED FROM PYTHON SOURCE LINES 81-82

Setup preprocessing

.. GENERATED FROM PYTHON SOURCE LINES 82-85

.. code-block:: Python
   :lineno-start: 82

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


.. GENERATED FROM PYTHON SOURCE LINES 86-87

Setup datasets

.. GENERATED FROM PYTHON SOURCE LINES 87-110

.. code-block:: Python
   :lineno-start: 88


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

    # create all OOD datasets
    ood_datasets = [
        Textures,
        TinyImageNetCrop,
        TinyImageNetResize,
        LSUNCrop,
        LSUNResize,
        Places365,
        CIFAR100,
        MNIST,
        FashionMNIST,
    ]
    datasets = {}
    for ood_dataset in ood_datasets:
        dataset_out_test = ood_dataset(
            root="data", transform=trans, target_transform=ToUnknown(), download=True
        )
        test_loader = DataLoader(dataset_in_test + dataset_out_test, batch_size=512, num_workers=12)
        datasets[ood_dataset.__name__] = test_loader


.. GENERATED FROM PYTHON SOURCE LINES 111-112

**Stage 1**: Create DNN with pre-trained weights from the Hendrycks baseline paper

.. GENERATED FROM PYTHON SOURCE LINES 112-115

.. code-block:: Python
   :lineno-start: 112

    print("STAGE 1: Creating a Model")
    model = WideResNet(num_classes=10, pretrained="cifar10-pt").eval().to(device)


.. GENERATED FROM PYTHON SOURCE LINES 116-117

**Stage 2**: Create OOD detector

.. GENERATED FROM PYTHON SOURCE LINES 117-164

.. code-block:: Python
   :lineno-start: 117

    print("STAGE 2: Creating OOD Detectors")
    detectors = {}
    detectors["Entropy"] = Entropy(model)
    detectors["ViM"] = ViM(model.features, d=64, w=model.fc.weight, b=model.fc.bias)
    detectors["Mahalanobis+ODIN"] = Mahalanobis(model.features, norm_std=norm_std, eps=0.002)
    detectors["Mahalanobis"] = Mahalanobis(model.features)

    detectors["KLMatching"] = KLMatching(model)
    detectors["SHE"] = SHE(model.features, model.fc)
    detectors["MSP"] = MaxSoftmax(model)
    detectors["EnergyBased"] = EnergyBased(model)
    detectors["MaxLogit"] = MaxLogit(model)
    detectors["ODIN"] = ODIN(model, norm_std=norm_std, eps=0.002)
    detectors["DICE"] = DICE(model=model.features, w=model.fc.weight, b=model.fc.bias, p=0.65)
    detectors["RMD"] = RMD(model.features)

    detectors["MultiMahalanobis"] = MultiMahalanobis(
        [
            model.conv1,
            model.block1,
            model.block2,
            model.block3,
            nn.Sequential(model.bn1, model.relu),
        ]
    )
    detectors["Gram"] = Gram(
        num_classes=10,
        head=nn.Sequential(nn.AdaptiveAvgPool2d(1), nn.Flatten(), model.fc),
        feature_layers=[
            model.conv1,
            model.block1,
            model.block2,
            model.block3,
            nn.Sequential(model.bn1, model.relu),
        ],
    )


    # fit detectors to training data (some require this, some do not)
    print(f"> Fitting {len(detectors)} detectors")
    loader_in_train = DataLoader(
        CIFAR10(root="data", train=True, transform=trans), batch_size=512, num_workers=12
    )
    for name, detector in detectors.items():
        print(f"--> Fitting {name}")
        detector.fit(loader_in_train, device=device)


.. GENERATED FROM PYTHON SOURCE LINES 165-166

**Stage 3**: Evaluate Detectors

.. GENERATED FROM PYTHON SOURCE LINES 166-188

.. code-block:: Python
   :lineno-start: 166

    print(f"STAGE 3: Evaluating {len(detectors)} detectors on {len(datasets)} datasets.")
    results = []

    with torch.no_grad():
        for detector_name, detector in detectors.items():
            print(f"> Evaluating {detector_name}")
            for dataset_name, loader in datasets.items():
                print(f"--> {dataset_name}")
                metrics = OODMetrics()
                for x, y in loader:
                    metrics.update(detector(x.to(device)), y.to(device))

                r = {"Detector": detector_name, "Dataset": dataset_name}
                r.update(metrics.compute())
                results.append(r)

    # calculate mean scores over all datasets, use percent
    df = pd.DataFrame(results)
    mean_scores = (
        df.groupby("Detector")[["AUROC", "AUTC", "AUPR-IN", "AUPR-OUT", "FPR95TPR"]].mean() * 100
    )
    print(mean_scores.sort_values("AUROC").to_csv(float_format="%.2f"))


.. _sphx_glr_download_auto_examples_benchmarks_manual_cifar10_baseline.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: cifar10_baseline.ipynb <cifar10_baseline.ipynb>`

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

      :download:`Download Python source code: cifar10_baseline.py <cifar10_baseline.py>`

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

      :download:`Download zipped: cifar10_baseline.zip <cifar10_baseline.zip>`


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

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