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

.. only:: html

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

        New to DeepInverse? Get started with the basics with the
        :ref:`5 minute quickstart tutorial <sphx_glr_auto_examples_basics_demo_quickstart.py>`.

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

.. _sphx_glr_auto_examples_self-supervised-learning_demo_transforms.py:


Image transforms for equivariance & augmentations
=================================================

We demonstrate the use of our ``deepinv.transform`` module for use in
solving imaging problems. These can be used for:

1. Data augmentation (similar to ``torchvision.transforms``)
2. Building equivariant denoisers
   (:class:`deepinv.models.EquivariantDenoiser`) for robust denoising
   (e.g from :footcite:t:`terris2024equivariant`)
3. Self-supervised learning using Equivariant Imaging from :footcite:t:`chen2021equivariant`. See
   :ref:`sphx_glr_auto_examples_self-supervised-learning_demo_ei_transforms.py`,
   :ref:`sphx_glr_auto_examples_self-supervised-learning_demo_equivariant_imaging.py`
   for thorough examples.

See :ref:`docs <transform>` for full list of implemented transforms.

1. Data augmentation
--------------------

We can use ``deepinv`` transforms in the same way as ``torchvision``
transforms, and chain them together for data augmentation. Our
transforms are customisable and offer some group-theoretic properties.

We demonstrate a random roto-scale combined with a random masking, and a
constrained pixel-shift with a random color jitter.
Note that all our transforms can easily be inverted using the method ``transform.inverse()``.

First, load a sample image.

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.. code-block:: Python


    import deepinv as dinv
    from torchvision.transforms import Compose, ColorJitter, RandomErasing, Resize

    x = dinv.utils.load_example("celeba_example.jpg")

    # Random roto-scale with random masking
    transform = Compose(
        [
            dinv.transform.Rotate() * dinv.transform.Scale(),
            RandomErasing(),
        ]
    )

    # Constrained pixel-shift with a random color jitter
    transform2 = Compose(
        [
            dinv.transform.Shift(shift_max=0.2),
            ColorJitter(hue=0.5),
        ]
    )

    # Random diffeomorphism
    transform3 = dinv.transform.CPABDiffeomorphism()

    dinv.utils.plot(
        [x, transform(x), transform2(x), transform3(x)],
        titles=["Orig", "Transform 1", "Transform 2", "Transform 3"],
    )





.. image-sg:: /auto_examples/self-supervised-learning/images/sphx_glr_demo_transforms_001.png
   :alt: Orig, Transform 1, Transform 2, Transform 3
   :srcset: /auto_examples/self-supervised-learning/images/sphx_glr_demo_transforms_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /home/runner/work/deepinv/deepinv/deepinv/transform/rotate.py:46: UserWarning: The default interpolation mode will be changed to bilinear interpolation in the near future. Please specify the interpolation mode explicitly if you plan to keep using nearest interpolation.
      warn(




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By letting ``n_trans`` be equal to the full group size, all transforms
are recovered:


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.. code-block:: Python


    reflect = dinv.transform.Reflect(dim=[-2, -1], n_trans=4)
    rotate = dinv.transform.Rotate(multiples=90, positive=True, n_trans=4)
    dinv.utils.plot(
        [reflect(x), rotate(x)], titles=["Full 2D reflect group", "Full rotate group"]
    )





.. image-sg:: /auto_examples/self-supervised-learning/images/sphx_glr_demo_transforms_002.png
   :alt: Full 2D reflect group, Full rotate group
   :srcset: /auto_examples/self-supervised-learning/images/sphx_glr_demo_transforms_002.png
   :class: sphx-glr-single-img





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2. Equivariant denoiser or plug-and-play
----------------------------------------

Suppose we want to make a denoiser equivariant to the rotoreflect group,
taken as the group product of the 90 degree rotations (order 4) and 1D reflects (order 2).
We can do this with our transform arithmetic (note this results in the full dihedral group
:math:`\text{Dih}_4` of order 8):


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.. code-block:: Python


    transform = rotate * dinv.transform.Reflect(dim=[-1], n_trans=2)









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Let's simulate some Gaussian noise and turn a simple (median filter)
denoiser into an equivariant denoiser
(:class:`deepinv.models.EquivariantDenoiser`):


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.. code-block:: Python


    sigma = 0.1
    physics = dinv.physics.Denoising(noise_model=dinv.physics.GaussianNoise(sigma=sigma))
    y = physics(Resize(128)(x))

    model = dinv.models.MedianFilter()
    model_eq = dinv.models.EquivariantDenoiser(model, transform=transform)

    dinv.utils.plot([x, y, model(y, sigma=sigma), model_eq(y, sigma=sigma)])





.. image-sg:: /auto_examples/self-supervised-learning/images/sphx_glr_demo_transforms_003.png
   :alt: demo transforms
   :srcset: /auto_examples/self-supervised-learning/images/sphx_glr_demo_transforms_003.png
   :class: sphx-glr-single-img





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What's going on under the hood? We use the ``transform.symmetrize``
method to symmetrize the function :math:`f` with respect to a projective
transform (with a Monte Carlo approach of ``n_trans=2`` transforms per call):


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.. code-block:: Python


    # Example non-equivariant function
    f = lambda x: x[..., [0]] * x

    # Example non-linear transform with n=2
    t = dinv.transform.projective.PanTiltRotate(n_trans=2, theta_max=10, theta_z_max=0)

    # Symmetrize function with respect to transform
    f_s = t.symmetrize(f, average=True)
    dinv.utils.plot(
        [x, f(x), f_s(x)], titles=["Orig", "$f(x)$", "$\\sum_i T_i^{-1}f(T_ix)$"]
    )





.. image-sg:: /auto_examples/self-supervised-learning/images/sphx_glr_demo_transforms_004.png
   :alt: Orig, $f(x)$, $\sum_i T_i^{-1}f(T_ix)$
   :srcset: /auto_examples/self-supervised-learning/images/sphx_glr_demo_transforms_004.png
   :class: sphx-glr-single-img





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3. Equivariant imaging
----------------------

We can also use our transforms to create the self-supervised equivariant
imaging loss. See
:ref:`sphx_glr_auto_examples_self-supervised-learning_demo_ei_transforms.py`,
:ref:`sphx_glr_auto_examples_self-supervised-learning_demo_equivariant_imaging.py`
for examples of self-supervised learning for MRI and inpainting. For
example, the EI loss can easily be defined using any combination of
transforms:


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.. code-block:: Python


    loss = dinv.loss.EILoss(
        transform=dinv.transform.projective.Affine() | dinv.transform.projective.Euclidean()
    )








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:References:

.. footbibliography::


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 4.560 seconds)


.. _sphx_glr_download_auto_examples_self-supervised-learning_demo_transforms.py:

.. only:: html

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

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