.. 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_r2r_denoising.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_self-supervised-learning_demo_r2r_denoising.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_self-supervised-learning_demo_r2r_denoising.py:


Self-supervised denoising with the Generalized R2R loss.
====================================================================================================

This example shows you how to train a denoiser network in a fully self-supervised way,
using noisy images only via the `Generalized Recorrupted2Recorrupted (GR2R) loss <https://arxiv.org/abs/2412.04648>`_,
which exploits knowledge about the noise distribution. You can change the noise distribution by selecting
from predefined noise models such as Gaussian, Poisson, and Gamma noise.

.. GENERATED FROM PYTHON SOURCE LINES 10-21

.. code-block:: Python


    from pathlib import Path

    import torch
    from torch.utils.data import DataLoader
    from torchvision import transforms, datasets

    import deepinv as dinv
    from deepinv.utils.demo import get_data_home
    from deepinv.models.utils import get_weights_url








.. GENERATED FROM PYTHON SOURCE LINES 22-25

Setup paths for data loading and results.
---------------------------------------------------------------


.. GENERATED FROM PYTHON SOURCE LINES 25-37

.. code-block:: Python


    BASE_DIR = Path(".")
    DATA_DIR = BASE_DIR / "measurements"
    CKPT_DIR = BASE_DIR / "ckpts"
    ORIGINAL_DATA_DIR = get_data_home()

    # Set the global random seed from pytorch to ensure reproducibility of the example.
    torch.manual_seed(0)

    device = dinv.utils.get_freer_gpu() if torch.cuda.is_available() else "cpu"
    print(device)





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

 .. code-block:: none

    cpu




.. GENERATED FROM PYTHON SOURCE LINES 38-42

Load base image datasets
----------------------------------------------------------------------------------
In this example, we use the MNIST dataset as the base image dataset.


.. GENERATED FROM PYTHON SOURCE LINES 42-55

.. code-block:: Python


    operation = "denoising"
    train_dataset_name = "MNIST"

    transform = transforms.Compose([transforms.ToTensor()])

    train_dataset = datasets.MNIST(
        root=ORIGINAL_DATA_DIR, train=True, transform=transform, download=True
    )
    test_dataset = datasets.MNIST(
        root=ORIGINAL_DATA_DIR, train=False, transform=transform, download=True
    )








.. GENERATED FROM PYTHON SOURCE LINES 56-66

Generate a dataset of noisy images
----------------------------------------------------------------------------------

Generate a dataset of noisy images corrupted by Poisson noise.
The predefined noise models in the physics module include Gaussian, Poisson, and Gamma noise.
Here, we use Poisson noise as an example, but you can also use Gaussian or Gamma noise.
.. note::

      We use a subset of the whole training set to reduce the computational load of the example.
      We recommend to use the whole set by setting ``n_images_max=None`` to get the best results.

.. GENERATED FROM PYTHON SOURCE LINES 66-102

.. code-block:: Python


    # defined physics
    predefined_noise_models = dict(
        gaussian=dinv.physics.GaussianNoise(sigma=0.1),
        poisson=dinv.physics.PoissonNoise(gain=0.5),
        gamma=dinv.physics.GammaNoise(l=10.0),
    )

    noise_name = "poisson"
    noise_model = predefined_noise_models[noise_name]
    physics = dinv.physics.Denoising(noise_model)

    # Use parallel dataloader if using a GPU to fasten training,
    # otherwise, as all computes are on CPU, use synchronous data loading.
    num_workers = 4 if torch.cuda.is_available() else 0

    n_images_max = (
        100 if torch.cuda.is_available() else 5
    )  # number of images used for training

    measurement_dir = DATA_DIR / train_dataset_name / operation
    deepinv_datasets_path = dinv.datasets.generate_dataset(
        train_dataset=train_dataset,
        test_dataset=test_dataset,
        physics=physics,
        device=device,
        save_dir=measurement_dir,
        train_datapoints=n_images_max,
        test_datapoints=n_images_max,
        num_workers=num_workers,
        dataset_filename="demo_r2r",
    )

    train_dataset = dinv.datasets.HDF5Dataset(path=deepinv_datasets_path, train=True)
    test_dataset = dinv.datasets.HDF5Dataset(path=deepinv_datasets_path, train=False)





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

 .. code-block:: none

    Dataset has been saved at measurements/MNIST/denoising/demo_r2r0.h5




.. GENERATED FROM PYTHON SOURCE LINES 103-107

Set up the denoiser network
---------------------------------------------------------------

We use a simple U-Net architecture with 2 scales as the denoiser network.

.. GENERATED FROM PYTHON SOURCE LINES 107-113

.. code-block:: Python


    model = dinv.models.ArtifactRemoval(
        dinv.models.UNet(in_channels=1, out_channels=1, scales=2).to(device)
    )









.. GENERATED FROM PYTHON SOURCE LINES 114-122

Set up the training parameters
--------------------------------------------
We set :class:`deepinv.loss.R2RLoss` as the training loss.

.. note::

      There are GR2R losses for various noise distributions, which can be specified by the noise model.


.. GENERATED FROM PYTHON SOURCE LINES 122-146

.. code-block:: Python


    epochs = 1  # choose training epochs
    learning_rate = 1e-4
    batch_size = 32 if torch.cuda.is_available() else 1

    # choose self-supervised training loss
    loss = dinv.loss.R2RLoss(noise_model=noise_model)
    model = loss.adapt_model(model)  # important step!

    # choose optimizer and scheduler
    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=1e-8)
    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=int(epochs * 0.8) + 1)

    # # start with a pretrained model to reduce training time

    if noise_name == "poisson":
        file_name = "ckp_10_demo_r2r_poisson.pth"
        url = get_weights_url(model_name="demo", file_name=file_name)
        ckpt = torch.hub.load_state_dict_from_url(
            url, map_location=lambda storage, loc: storage, file_name=file_name
        )
        # load a checkpoint to reduce training time
        model.load_state_dict(ckpt["state_dict"])





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

 .. code-block:: none

    Downloading: "https://huggingface.co/deepinv/demo/resolve/main/ckp_10_demo_r2r_poisson.pth?download=true" to /home/runner/.cache/torch/hub/checkpoints/ckp_10_demo_r2r_poisson.pth

      0%|          | 0.00/5.14M [00:00<?, ?B/s]
    100%|██████████| 5.14M/5.14M [00:00<00:00, 55.5MB/s]




.. GENERATED FROM PYTHON SOURCE LINES 147-151

Train the network
--------------------------------------------



.. GENERATED FROM PYTHON SOURCE LINES 151-184

.. code-block:: Python


    verbose = True  # print training information
    wandb_vis = False  # plot curves and images in Weight&Bias

    train_dataloader = DataLoader(
        train_dataset, batch_size=batch_size, num_workers=num_workers, shuffle=True
    )
    test_dataloader = DataLoader(
        test_dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False
    )

    # Initialize the trainer
    trainer = dinv.Trainer(
        model=model,
        physics=physics,
        epochs=epochs,
        scheduler=scheduler,
        losses=loss,
        optimizer=optimizer,
        device=device,
        train_dataloader=train_dataloader,
        eval_dataloader=test_dataloader,
        plot_images=True,
        save_path=str(CKPT_DIR / operation),
        verbose=verbose,
        show_progress_bar=False,  # disable progress bar for better vis in sphinx gallery.
        wandb_vis=wandb_vis,
    )

    # Train the network
    model = trainer.train()





.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /auto_examples/self-supervised-learning/images/sphx_glr_demo_r2r_denoising_001.png
         :alt: Ground truth, Measurement, Reconstruction
         :srcset: /auto_examples/self-supervised-learning/images/sphx_glr_demo_r2r_denoising_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/self-supervised-learning/images/sphx_glr_demo_r2r_denoising_002.png
         :alt: Ground truth, Measurement, Reconstruction
         :srcset: /auto_examples/self-supervised-learning/images/sphx_glr_demo_r2r_denoising_002.png
         :class: sphx-glr-multi-img


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

 .. code-block:: none

    The model has 444737 trainable parameters
    Train epoch 0: TotalLoss=0.149, PSNR=13.407
    Eval epoch 0: PSNR=16.052




.. GENERATED FROM PYTHON SOURCE LINES 185-189

Test the network
--------------------------------------------



.. GENERATED FROM PYTHON SOURCE LINES 189-191

.. code-block:: Python


    trainer.test(test_dataloader)



.. image-sg:: /auto_examples/self-supervised-learning/images/sphx_glr_demo_r2r_denoising_003.png
   :alt: Ground truth, Measurement, No learning, Reconstruction
   :srcset: /auto_examples/self-supervised-learning/images/sphx_glr_demo_r2r_denoising_003.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    Eval epoch 0: PSNR=15.688, PSNR no learning=12.709
    Test results:
    PSNR no learning: 12.709 +- 2.129
    PSNR: 15.688 +- 1.777

    {'PSNR no learning': np.float64(12.70888900756836), 'PSNR no learning_std': np.float64(2.129271799567431), 'PSNR': np.float64(15.6879638671875), 'PSNR_std': np.float64(1.7765691388022045)}




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

   **Total running time of the script:** (0 minutes 4.033 seconds)


.. _sphx_glr_download_auto_examples_self-supervised-learning_demo_r2r_denoising.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: demo_r2r_denoising.ipynb <demo_r2r_denoising.ipynb>`

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

      :download:`Download Python source code: demo_r2r_denoising.py <demo_r2r_denoising.py>`

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

      :download:`Download zipped: demo_r2r_denoising.zip <demo_r2r_denoising.zip>`


.. only:: html

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

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