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

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

.. _sphx_glr_auto_examples_unfolded_demo_ram.py:


Reconstruct Anything Model (RAM) for solving inverse problems.
====================================================================================================

This example shows how to use the RAM foundation model to solve inverse problems. The RAM model, described in
the following `paper <https://arxiv.org/abs/2503.08915>`_, is a modified DRUNet architecture that is trained on
a large number of inverse problems.

.. GENERATED FROM PYTHON SOURCE LINES 9-52

.. code-block:: Python


    import torch
    import deepinv as dinv
    from deepinv.models import RAM

    device = "cuda" if torch.cuda.is_available() else "cpu"

    # Load the pretrained model
    model = RAM(device=device)

    # load image
    x = dinv.utils.load_example("butterfly.png", img_size=(127, 129)).to(device)

    # create forward operator
    physics = dinv.physics.Inpainting(
        img_size=(3, 127, 129),
        mask=0.3,
        noise_model=dinv.physics.GaussianNoise(0.05),
        device=device,
    )

    # generate measurement
    y = physics(x)

    # run inference
    with torch.no_grad():
        x_hat = model(y, physics=physics)

    # compute PSNR
    in_psnr = dinv.metric.PSNR()(x, y).item()
    out_psnr = dinv.metric.PSNR()(x, x_hat).item()

    # plot
    dinv.utils.plot(
        [x, y, x_hat],
        [
            "Original",
            "Measurement\n PSNR = {:.2f}dB".format(in_psnr),
            "Reconstruction\n PSNR = {:.2f}dB".format(out_psnr),
        ],
        figsize=(8, 3),
    )




.. image-sg:: /auto_examples/unfolded/images/sphx_glr_demo_ram_001.png
   :alt: Original, Measurement  PSNR = 6.03dB, Reconstruction  PSNR = 25.80dB
   :srcset: /auto_examples/unfolded/images/sphx_glr_demo_ram_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    Downloading: "https://huggingface.co/mterris/ram/resolve/main/ram.pth.tar" to /home/runner/.cache/torch/hub/checkpoints/ram.pth.tar

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     79%|███████▉  | 108M/136M [00:00<00:00, 382MB/s] 
    100%|██████████| 136M/136M [00:00<00:00, 382MB/s]




.. GENERATED FROM PYTHON SOURCE LINES 53-54

This model was also trained on various denoising problems, in particular on Poisson-Gaussian denoising.

.. GENERATED FROM PYTHON SOURCE LINES 54-84

.. code-block:: Python


    sigma, gain = 0.2, 0.5
    physics = dinv.physics.Denoising(
        noise_model=dinv.physics.PoissonGaussianNoise(sigma=sigma, gain=gain),
    )

    # generate measurement
    y = physics(x)

    # run inference
    with torch.no_grad():
        x_hat = model(y, physics=physics)
        # or alternatively, we can use the model without physics:
        # x_hat = model(y, sigma=sigma, gain=gain)

    # compute PSNR
    in_psnr = dinv.metric.PSNR()(x, y).item()
    out_psnr = dinv.metric.PSNR()(x, x_hat).item()

    # plot
    dinv.utils.plot(
        [x, y, x_hat],
        [
            "Original",
            "Measurement\n PSNR = {:.2f}dB".format(in_psnr),
            "Reconstruction\n PSNR = {:.2f}dB".format(out_psnr),
        ],
        figsize=(8, 3),
    )




.. image-sg:: /auto_examples/unfolded/images/sphx_glr_demo_ram_002.png
   :alt: Original, Measurement  PSNR = 5.20dB, Reconstruction  PSNR = 24.42dB
   :srcset: /auto_examples/unfolded/images/sphx_glr_demo_ram_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 85-88

This model is not trained on all degradations, so it may not perform well on all inverse problems out-of-the-box.
For instance, it is not trained on image demosaicing. Applying it to a demosaicing problem out-of-the-box will yield poor results,
as shown in the following example:

.. GENERATED FROM PYTHON SOURCE LINES 88-118

.. code-block:: Python



    # Define the Demosaicing physics
    physics = dinv.physics.Demosaicing(
        img_size=x.shape[1:], noise_model=dinv.physics.PoissonNoise(0.1), device=device
    )

    # generate measurement
    y = physics(x)

    # run inference
    with torch.no_grad():
        x_hat = model(y, physics=physics)

    # compute PSNR
    in_psnr = dinv.metric.PSNR()(x, y).item()
    out_psnr = dinv.metric.PSNR()(x, x_hat).item()

    # plot
    dinv.utils.plot(
        [x, y, x_hat],
        [
            "Original",
            "Measurement\n PSNR = {:.2f}dB".format(in_psnr),
            "0 shot reconstruction\n PSNR = {:.2f}dB".format(out_psnr),
        ],
        figsize=(8, 3),
    )





.. image-sg:: /auto_examples/unfolded/images/sphx_glr_demo_ram_003.png
   :alt: Original, Measurement  PSNR = 5.99dB, 0 shot reconstruction  PSNR = 21.78dB
   :srcset: /auto_examples/unfolded/images/sphx_glr_demo_ram_003.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 119-126

To improve results, we can fine-tune the model on the specific degradation for the sample of interest.
This can be done even in the absence of ground truth data, using unsupervised training.
We showcase this in the following, where the model is fine-tuned on the measurement vector `y` itself.
Here, since this example is run in a no-GPU environment, we will use a small patch of the image to speed up training,
but in practice, we can use the full image.

First, we will create a dataset for unsupervised training that

.. GENERATED FROM PYTHON SOURCE LINES 126-164

.. code-block:: Python



    class UnsupDataset(torch.utils.data.Dataset):
        r"""
        Dataset for unsupervised learning tasks.

        This dataset is used to return only the data without any labels.

        :param torch.Tensor data: Input data tensor of shape (N, ...), where N is the number of samples and ... represents the data dimensions.
        """

        def __init__(self, data):
            self.data = data

        def __len__(self):
            return self.data.size(0)

        def __getitem__(self, idx):
            return torch.nan, self.data[idx]


    physics_train = dinv.physics.Demosaicing(
        img_size=(3, 64, 64),
        noise_model=dinv.physics.PoissonNoise(0.1, clip_positive=True),
        device=device,
    )
    x_train = x[..., :64, :64]  # take a small patch of the image
    y_train = physics_train(x_train)

    losses = [
        dinv.loss.R2RLoss(),
        dinv.loss.EILoss(dinv.transform.Shift(shift_max=0.4), weight=0.1),
    ]

    dataset = UnsupDataset(y_train)

    train_dataloader = torch.utils.data.DataLoader(dataset)








.. GENERATED FROM PYTHON SOURCE LINES 165-168

In order to check the performance of the fine-tuned model, we will use a validation set.
We will use a small patch of another image. Note that this validation is also performed in an unsupervised manner,
so we will not use the ground truth validation image.

.. GENERATED FROM PYTHON SOURCE LINES 168-192

.. code-block:: Python

    y_val = physics_train(dinv.utils.load_example("leaves.png")[..., :64, :64].to(device))
    eval_dataloader = torch.utils.data.DataLoader(UnsupDataset(y_val))

    optimizer = torch.optim.Adam(model.parameters(), lr=5e-5)

    max_epochs = 20
    trainer = dinv.Trainer(
        model=model,
        physics=physics_train,
        eval_interval=5,
        ckp_interval=max_epochs - 1,
        metrics=losses[0],
        early_stop=True,
        device=device,
        losses=losses,
        epochs=max_epochs,
        optimizer=optimizer,
        train_dataloader=train_dataloader,
        eval_dataloader=eval_dataloader,
    )

    # finetune
    finetuned_model = trainer.train()





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

 .. code-block:: none

    The model has 35618953 trainable parameters

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    Best model saved at epoch 1

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    Train epoch 16/20:   0%|                                                       | 0/1 [00:01<?, ?it/s, R2RLoss=0.128, EILoss=0.000205, TotalLoss=0.128]
    Train epoch 16/20: 100%|███████████████████████████████████████████████| 1/1 [00:01<00:00,  1.55s/it, R2RLoss=0.128, EILoss=0.000205, TotalLoss=0.128]
    Train epoch 16/20: 100%|███████████████████████████████████████████████| 1/1 [00:01<00:00,  1.55s/it, R2RLoss=0.128, EILoss=0.000205, TotalLoss=0.128]

      0%|                                                                                                                           | 0/1 [00:00<?, ?it/s]
    Eval epoch 16/20:   0%|                                                                                                         | 0/1 [00:00<?, ?it/s]
    Eval epoch 16/20:   0%|                                                                                           | 0/1 [00:01<?, ?it/s, R2RLoss=3.76]
    Eval epoch 16/20: 100%|███████████████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00,  1.40s/it, R2RLoss=3.76]
    Eval epoch 16/20: 100%|███████████████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00,  1.40s/it, R2RLoss=3.76]
    Early stopping triggered as validation metrics have not improved in the last 3 validation steps, disable it with early_stop=False




.. GENERATED FROM PYTHON SOURCE LINES 193-194

We can now use the fine-tuned model to reconstruct the image from the measurement vector `y`.

.. GENERATED FROM PYTHON SOURCE LINES 194-212

.. code-block:: Python


    with torch.no_grad():
        x_hat = finetuned_model(y, physics=physics)

    # compute PSNR
    in_psnr = dinv.metric.PSNR()(x, y).item()
    out_psnr = dinv.metric.PSNR()(x, x_hat).item()

    # plot
    dinv.utils.plot(
        [x, y, x_hat],
        [
            "Original",
            "Measurement\n PSNR = {:.2f}dB".format(in_psnr),
            "Finetuned reconstruction\n PSNR = {:.2f}dB".format(out_psnr),
        ],
        figsize=(8, 3),
    )



.. image-sg:: /auto_examples/unfolded/images/sphx_glr_demo_ram_004.png
   :alt: Original, Measurement  PSNR = 5.99dB, Finetuned reconstruction  PSNR = 24.60dB
   :srcset: /auto_examples/unfolded/images/sphx_glr_demo_ram_004.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 39.341 seconds)


.. _sphx_glr_download_auto_examples_unfolded_demo_ram.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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