Tutorial: Unstructured convolutional autoencoder via continuous convolution
===========================================================================

In this tutorial, we will show how to use the Continuous Convolutional
Filter, and how to build common Deep Learning architectures with it. The
implementation of the filter follows the original work `A Continuous
Convolutional Trainable Filter for Modelling Unstructured
Data <https://arxiv.org/abs/2210.13416>`__.

First of all we import the modules needed for the tutorial:

.. code:: ipython3

    import torch 
    import matplotlib.pyplot as plt 
    from pina.problem import AbstractProblem
    from pina.solvers import SupervisedSolver
    from pina.trainer import Trainer
    from pina import Condition, LabelTensor
    from pina.model.layers import ContinuousConvBlock 
    import torchvision # for MNIST dataset
    from pina.model import FeedForward # for building AE and MNIST classification

The tutorial is structured as follow: 

* `Continuous filter background <#continuous-filter-background>`__: understand how the convolutional filter works and how to use it. 
* `Building a MNIST Classifier <#building-a-mnist-classifier>`__: show how to build a simple
  classifier using the MNIST dataset and how to combine a continuous
  convolutional layer with a feedforward neural network. 
* `Building a Continuous Convolutional Autoencoder <#building-a-continuous-convolutional-autoencoder>`__: show
  show to use the continuous filter to work with unstructured data for
  autoencoding and up-sampling.

Continuous filter background
----------------------------

As reported by the authors in the original paper: in contrast to
discrete convolution, continuous convolution is mathematically defined
as:

.. math::


       \mathcal{I}_{\rm{out}}(\mathbf{x}) = \int_{\mathcal{X}}  \mathcal{I}(\mathbf{x} + \mathbf{\tau}) \cdot \mathcal{K}(\mathbf{\tau}) d\mathbf{\tau},

where :math:`\mathcal{K} : \mathcal{X} \rightarrow \mathbb{R}` is the
*continuous filter* function, and
:math:`\mathcal{I} : \Omega \subset \mathbb{R}^N \rightarrow \mathbb{R}`
is the input function. The continuous filter function is approximated
using a FeedForward Neural Network, thus trainable during the training
phase. The way in which the integral is approximated can be different,
currently on **PINA** we approximate it using a simple sum, as suggested
by the authors. Thus, given :math:`\{\mathbf{x}_i\}_{i=1}^{n}` points in
:math:`\mathbb{R}^N` of the input function mapped on the
:math:`\mathcal{X}` filter domain, we approximate the above equation as:

.. math::


       \mathcal{I}_{\rm{out}}(\mathbf{\tilde{x}}_i) = \sum_{{\mathbf{x}_i}\in\mathcal{X}}  \mathcal{I}(\mathbf{x}_i + \mathbf{\tau}) \cdot \mathcal{K}(\mathbf{x}_i),

where :math:`\mathbf{\tau} \in \mathcal{S}`, with :math:`\mathcal{S}`
the set of available strides, corresponds to the current stride position
of the filter, and :math:`\mathbf{\tilde{x}}_i` points are obtained by
taking the centroid of the filter position mapped on the :math:`\Omega`
domain.

We will now try to pratically see how to work with the filter. From the
above definition we see that what is needed is: 1. A domain and a
function defined on that domain (the input) 2. A stride, corresponding
to the positions where the filter needs to be :math:`\rightarrow`
``stride`` variable in ``ContinuousConv`` 3. The filter rectangular
domain :math:`\rightarrow` ``filter_dim`` variable in ``ContinuousConv``

Input function
~~~~~~~~~~~~~~

The input function for the continuous filter is defined as a tensor of
shape:

.. math:: [B \times N_{in} \times N \times D]

\ where :math:`B` is the batch_size, :math:`N_{in}` is the number of
input fields, :math:`N` the number of points in the mesh, :math:`D` the
dimension of the problem. In particular: \* :math:`D` is the number of
spatial variables + 1. The last column must contain the field value. For
example for 2D problems :math:`D=3` and the tensor will be something
like ``[first coordinate, second coordinate, field value]`` \*
:math:`N_{in}` represents the number of vectorial function presented.
For example a vectorial function :math:`f = [f_1, f_2]` will have
:math:`N_{in}=2`

Let’s see an example to clear the ideas. We will be verbose to explain
in details the input form. We wish to create the function:

.. math::


   f(x, y) = [\sin(\pi x) \sin(\pi y), -\sin(\pi x) \sin(\pi y)] \quad (x,y)\in[0,1]\times[0,1]

using a batch size of one.

.. code:: ipython3

    # batch size fixed to 1
    batch_size = 1
    
    # points in the mesh fixed to 200
    N = 200
    
    # vectorial 2 dimensional function, number_input_fileds=2
    number_input_fileds = 2
    
    # 2 dimensional spatial variables, D = 2 + 1 = 3
    D = 3
    
    # create the function f domain as random 2d points in [0, 1]
    domain = torch.rand(size=(batch_size, number_input_fileds, N, D-1))
    print(f"Domain has shape: {domain.shape}")
    
    # create the functions
    pi = torch.acos(torch.tensor([-1.])) # pi value
    f1 = torch.sin(pi * domain[:, 0, :, 0]) * torch.sin(pi * domain[:, 0, :, 1])
    f2 = - torch.sin(pi * domain[:, 1, :, 0]) * torch.sin(pi * domain[:, 1, :, 1])
    
    # stacking the input domain and field values
    data = torch.empty(size=(batch_size, number_input_fileds, N, D))
    data[..., :-1] = domain # copy the domain
    data[:, 0, :, -1] = f1 # copy first field value
    data[:, 1, :, -1] = f1  # copy second field value
    print(f"Filter input data has shape: {data.shape}")


.. parsed-literal::

    Domain has shape: torch.Size([1, 2, 200, 2])
    Filter input data has shape: torch.Size([1, 2, 200, 3])


Stride
~~~~~~

The stride is passed as a dictionary ``stride`` which tells the filter
where to go. Here is an example for the :math:`[0,1]\times[0,5]` domain:

.. code:: python

   # stride definition
   stride = {"domain": [1, 5],
             "start": [0, 0],
             "jump": [0.1, 0.3],
             "direction": [1, 1],
             }

This tells the filter:

1. ``domain``: square domain (the only implemented) :math:`[0,1]\times[0,5]`. The minimum value is always zero,
   while the maximum is specified by the user
2. ``start``: start position
   of the filter, coordinate :math:`(0, 0)` 
3. ``jump``: the jumps of the
   centroid of the filter to the next position :math:`(0.1, 0.3)` 
4. ``direction``: the directions of the jump, with ``1 = right``, 
   ``0 = no jump``,\ ``-1 = left`` with respect to the current position

**Note**

We are planning to release the possibility to directly pass a list of
possible strides!

Filter definition
~~~~~~~~~~~~~~~~~

Having defined all the previous blocks we are able to construct the
continuous filter. Suppose we would like to get an ouput with only one field, and let us
fix the filter dimension to be :math:`[0.1, 0.1]`.

.. code:: ipython3

    # filter dim
    filter_dim = [0.1, 0.1]
    
    # stride
    stride = {"domain": [1, 1],
              "start": [0, 0],
              "jump": [0.08, 0.08],
              "direction": [1, 1],
              }
    
    # creating the filter         
    cConv = ContinuousConvBlock(input_numb_field=number_input_fileds,
                            output_numb_field=1,
                            filter_dim=filter_dim,
                            stride=stride)
                            

That’s it! In just one line of code we have created the continuous
convolutional filter. By default the ``pina.model.FeedForward`` neural
network is intitialised, more on the
`documentation <https://mathlab.github.io/PINA/_rst/fnn.html>`__. In
case the mesh doesn’t change during training we can set the ``optimize``
flag equals to ``True``, to exploit optimizations for finding the points
to convolve.

.. code:: ipython3

    # creating the filter + optimization
    cConv = ContinuousConvBlock(input_numb_field=number_input_fileds,
                           output_numb_field=1,
                           filter_dim=filter_dim,
                           stride=stride,
                           optimize=True)


Let’s try to do a forward pass

.. code:: ipython3

    print(f"Filter input data has shape: {data.shape}")
    
    #input to the filter
    output = cConv(data)
    
    print(f"Filter output data has shape: {output.shape}")


.. parsed-literal::

    Filter input data has shape: torch.Size([1, 2, 200, 3])
    Filter output data has shape: torch.Size([1, 1, 169, 3])


If we don’t want to use the default ``FeedForward`` neural network, we
can pass a specified torch model in the ``model`` keyword as follow:

.. code:: ipython3

    class SimpleKernel(torch.nn.Module):
        def __init__(self) -> None:
            super().__init__()
            self. model = torch.nn.Sequential(
                torch.nn.Linear(2, 20),
                torch.nn.ReLU(),
                torch.nn.Linear(20, 20),
                torch.nn.ReLU(),
                torch.nn.Linear(20, 1))
    
        def forward(self, x):
            return self.model(x)
    
    
    cConv = ContinuousConvBlock(input_numb_field=number_input_fileds,
                           output_numb_field=1,
                           filter_dim=filter_dim,
                           stride=stride,
                           optimize=True,
                           model=SimpleKernel)


Notice that we pass the class and not an already built object!

Building a MNIST Classifier
---------------------------

Let’s see how we can build a MNIST classifier using a continuous
convolutional filter. We will use the MNIST dataset from PyTorch. In
order to keep small training times we use only 6000 samples for training
and 1000 samples for testing.

.. code:: ipython3

    from torch.utils.data import DataLoader, SubsetRandomSampler
    
    numb_training = 6000  # get just 6000 images for training
    numb_testing= 1000  # get just 1000 images for training
    seed = 111          # for reproducibility
    batch_size = 8      # setting batch size
    
    # setting the seed
    torch.manual_seed(seed)
    
    # downloading the dataset
    train_data = torchvision.datasets.MNIST('./data/', train=True, download=True,
                                            transform=torchvision.transforms.Compose([
                                                torchvision.transforms.ToTensor(),
                                                torchvision.transforms.Normalize(
                                                    (0.1307,), (0.3081,))
                                            ]))
    subsample_train_indices = torch.randperm(len(train_data))[:numb_training]
    train_loader = DataLoader(train_data, batch_size=batch_size,
                              sampler=SubsetRandomSampler(subsample_train_indices))
    
    test_data = torchvision.datasets.MNIST('./data/', train=False, download=True,
                                            transform=torchvision.transforms.Compose([
                                                torchvision.transforms.ToTensor(),
                                                torchvision.transforms.Normalize(
                                                    (0.1307,), (0.3081,))
                                            ]))
    subsample_test_indices = torch.randperm(len(train_data))[:numb_testing]
    test_loader = DataLoader(train_data, batch_size=batch_size,
                              sampler=SubsetRandomSampler(subsample_train_indices))

Let’s now build a simple classifier. The MNIST dataset is composed by
vectors of shape ``[batch, 1, 28, 28]``, but we can image them as one
field functions where the pixels :math:`ij` are the coordinate
:math:`x=i, y=j` in a :math:`[0, 27]\times[0,27]` domain, and the pixels
value are the field values. We just need a function to transform the
regular tensor in a tensor compatible for the continuous filter:

.. code:: ipython3

    def transform_input(x):
        batch_size = x.shape[0]
        dim_grid = tuple(x.shape[:-3:-1])
    
        # creating the n dimensional mesh grid for a single channel image
        values_mesh = [torch.arange(0, dim).float() for dim in dim_grid]
        mesh = torch.meshgrid(values_mesh)
        coordinates_mesh = [x.reshape(-1, 1) for x in mesh]
        coordinates = torch.cat(coordinates_mesh, dim=1).unsqueeze(
            0).repeat((batch_size, 1, 1)).unsqueeze(1)
    
        return torch.cat((coordinates, x.flatten(2).unsqueeze(-1)), dim=-1)
    
    
    # let's try it out
    image, s = next(iter(train_loader))
    print(f"Original MNIST image shape: {image.shape}")
    
    image_transformed = transform_input(image)
    print(f"Transformed MNIST image shape: {image_transformed.shape}")



.. parsed-literal::

    Original MNIST image shape: torch.Size([8, 1, 28, 28])
    Transformed MNIST image shape: torch.Size([8, 1, 784, 3])


We can now build a simple classifier! We will use just one convolutional
filter followed by a feedforward neural network

.. code:: ipython3

    # setting the seed
    torch.manual_seed(seed)
    
    class ContinuousClassifier(torch.nn.Module):
        def __init__(self):
            super().__init__()
    
            # number of classes for classification
            numb_class = 10
    
            # convolutional block
            self.convolution = ContinuousConvBlock(input_numb_field=1,
                                              output_numb_field=4,
                                              stride={"domain": [27, 27],
                                                      "start": [0, 0],
                                                      "jumps": [4, 4],
                                                      "direction": [1, 1.],
                                                      },
                                              filter_dim=[4, 4],
                                              optimize=True)
            # feedforward net
            self.nn = FeedForward(input_dimensions=196,
                                  output_dimensions=numb_class,
                                  layers=[120, 64],
                                  func=torch.nn.ReLU)
    
        def forward(self, x):
            # transform input + convolution
            x = transform_input(x)
            x = self.convolution(x)
            # feed forward classification
            return self.nn(x[..., -1].flatten(1))
    
    
    net = ContinuousClassifier()

Let’s try to train it using a simple pytorch training loop. We train for
juts 1 epoch using Adam optimizer with a :math:`0.001` learning rate.

.. code:: ipython3

    # setting the seed
    torch.manual_seed(seed)
    
    # optimizer and loss function
    optimizer = torch.optim.Adam(net.parameters(), lr=0.001)
    criterion = torch.nn.CrossEntropyLoss()
    
    for epoch in range(1):  # loop over the dataset multiple times
    
        running_loss = 0.0
        for i, data in enumerate(train_loader, 0):
            # get the inputs; data is a list of [inputs, labels]
            inputs, labels = data
    
            # zero the parameter gradients
            optimizer.zero_grad()
    
            # forward + backward + optimize
            outputs = net(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
    
            # print statistics
            running_loss += loss.item()
            if i % 50 == 49:    
                print(
                    f'batch [{i + 1}/{numb_training//batch_size}] loss[{running_loss / 500:.3f}]')
                running_loss = 0.0



.. parsed-literal::

    /u/d/dcoscia/.local/lib/python3.9/site-packages/torch/autograd/__init__.py:200: UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:109.)
      Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
    /u/d/dcoscia/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:546: UserWarning: Can't initialize NVML
      warnings.warn("Can't initialize NVML")


.. parsed-literal::

    batch [50/750] loss[0.161]
    batch [100/750] loss[0.073]
    batch [150/750] loss[0.063]
    batch [200/750] loss[0.051]
    batch [250/750] loss[0.044]
    batch [300/750] loss[0.050]
    batch [350/750] loss[0.053]
    batch [400/750] loss[0.049]
    batch [450/750] loss[0.046]
    batch [500/750] loss[0.034]
    batch [550/750] loss[0.036]
    batch [600/750] loss[0.040]
    batch [650/750] loss[0.028]
    batch [700/750] loss[0.040]
    batch [750/750] loss[0.040]


Let’s see the performance on the train set!

.. code:: ipython3

    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            images, labels = data
            # calculate outputs by running images through the network
            outputs = net(images)
            # the class with the highest energy is what we choose as prediction
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    
    print(
        f'Accuracy of the network on the 1000 test images: {(correct / total):.3%}')



.. parsed-literal::

    Accuracy of the network on the 1000 test images: 92.733%


As we can see we have very good performance for having traing only for 1
epoch! Nevertheless, we are still using structured data… Let’s see how
we can build an autoencoder for unstructured data now.

Building a Continuous Convolutional Autoencoder
-----------------------------------------------

Just as toy problem, we will now build an autoencoder for the following
function :math:`f(x,y)=\sin(\pi x)\sin(\pi y)` on the unit circle domain
centered in :math:`(0.5, 0.5)`. We will also see the ability to
up-sample (once trained) the results without retraining. Let’s first
create the input and visualize it, we will use firstly a mesh of
:math:`100` points.

.. code:: ipython3

    # create inputs
    def circle_grid(N=100):
        """Generate points withing a unit 2D circle centered in (0.5, 0.5)
    
            :param N: number of points
            :type N: float
            :return: [x, y] array of points
            :rtype: torch.tensor
            """
    
        PI = torch.acos(torch.zeros(1)).item() * 2
        R = 0.5
        centerX = 0.5
        centerY = 0.5
    
        r = R * torch.sqrt(torch.rand(N))
        theta = torch.rand(N) * 2 * PI
    
        x = centerX + r * torch.cos(theta)
        y = centerY + r * torch.sin(theta)
    
        return torch.stack([x, y]).T
    
    # create the grid
    grid = circle_grid(500)
    
    # create input
    input_data = torch.empty(size=(1, 1, grid.shape[0], 3))
    input_data[0, 0, :, :-1] = grid
    input_data[0, 0, :, -1] = torch.sin(pi * grid[:, 0]) * torch.sin(pi * grid[:, 1])
    
    # visualize data
    plt.title("Training sample with 500 points")
    plt.scatter(grid[:, 0], grid[:, 1], c=input_data[0, 0, :, -1])
    plt.colorbar()
    plt.show()




.. image:: tutorial_files/tutorial_32_0.png


Let’s now build a simple autoencoder using the continuous convolutional
filter. The data is clearly unstructured and a simple convolutional
filter might not work without projecting or interpolating first. Let’s
first build and ``Encoder`` and ``Decoder`` class, and then a
``Autoencoder`` class that contains both.

.. code:: ipython3

    class Encoder(torch.nn.Module):
        def __init__(self, hidden_dimension):
            super().__init__()
    
            # convolutional block
            self.convolution = ContinuousConvBlock(input_numb_field=1,
                                              output_numb_field=2,
                                              stride={"domain": [1, 1],
                                                      "start": [0, 0],
                                                      "jumps": [0.05, 0.05],
                                                      "direction": [1, 1.],
                                                      },
                                              filter_dim=[0.15, 0.15],
                                              optimize=True)
            # feedforward net
            self.nn = FeedForward(input_dimensions=400,
                                  output_dimensions=hidden_dimension,
                                  layers=[240, 120])
    
        def forward(self, x):
            # convolution
            x = self.convolution(x)
            # feed forward pass
            return self.nn(x[..., -1])
    
    
    class Decoder(torch.nn.Module):
        def __init__(self, hidden_dimension):
            super().__init__()
    
            # convolutional block
            self.convolution = ContinuousConvBlock(input_numb_field=2,
                                              output_numb_field=1,
                                              stride={"domain": [1, 1],
                                                      "start": [0, 0],
                                                      "jumps": [0.05, 0.05],
                                                      "direction": [1, 1.],
                                                      },
                                              filter_dim=[0.15, 0.15],
                                              optimize=True)
            # feedforward net
            self.nn = FeedForward(input_dimensions=hidden_dimension,
                                  output_dimensions=400,
                                  layers=[120, 240])
    
        def forward(self, weights, grid):
            # feed forward pass
            x = self.nn(weights)
            # transpose convolution
            return torch.sigmoid(self.convolution.transpose(x, grid))


Very good! Notice that in the ``Decoder`` class in the ``forward`` pass
we have used the ``.transpose()`` method of the
``ContinuousConvolution`` class. This method accepts the ``weights`` for
upsampling and the ``grid`` on where to upsample. Let’s now build the
autoencoder! We set the hidden dimension in the ``hidden_dimension``
variable. We apply the sigmoid on the output since the field value is
between :math:`[0, 1]`.

.. code:: ipython3

    class Autoencoder(torch.nn.Module):
        def __init__(self, hidden_dimension=10):
            super().__init__()
    
            self.encoder = Encoder(hidden_dimension)
            self.decoder = Decoder(hidden_dimension)
    
        def forward(self, x):
            # saving grid for later upsampling
            grid = x.clone().detach()
            # encoder
            weights = self.encoder(x)
            # decoder
            out = self.decoder(weights, grid)
            return out
    
    net = Autoencoder()

Let’s now train the autoencoder, minimizing the mean square error loss
and optimizing using Adam. We use the ``SupervisedSolver`` as solver,
and the problem is a simple problem created by inheriting from
``AbstractProblem``. It takes approximately two minutes to train on CPU.

.. code:: ipython3

    # define the problem
    class CircleProblem(AbstractProblem):
        input_variables = ['x', 'y', 'f']
        output_variables = input_variables
        conditions = {'data' : Condition(input_points=LabelTensor(input_data, input_variables), output_points=LabelTensor(input_data, output_variables))}
    
    # define the solver
    solver = SupervisedSolver(problem=CircleProblem(), model=net, loss=torch.nn.MSELoss())          
    
    # train
    trainer = Trainer(solver, max_epochs=150, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional)
    trainer.train()
            


.. parsed-literal::

    GPU available: False, used: False
    TPU available: False, using: 0 TPU cores
    IPU available: False, using: 0 IPUs
    HPU available: False, using: 0 HPUs



.. parsed-literal::

    Training: 0it [00:00, ?it/s]


.. parsed-literal::

    `Trainer.fit` stopped: `max_epochs=150` reached.


Let’s visualize the two solutions side by side!

.. code:: ipython3

    net.eval()
    
    # get output and detach from computational graph for plotting
    output = net(input_data).detach()
    
    # visualize data
    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))
    pic1 = axes[0].scatter(grid[:, 0], grid[:, 1], c=input_data[0, 0, :, -1])
    axes[0].set_title("Real")
    fig.colorbar(pic1)
    plt.subplot(1, 2, 2)
    pic2 = axes[1].scatter(grid[:, 0], grid[:, 1], c=output[0, 0, :, -1])
    axes[1].set_title("Autoencoder")
    fig.colorbar(pic2)
    plt.tight_layout()
    plt.show()




.. image:: tutorial_files/tutorial_40_0.png


As we can see the two are really similar! We can compute the :math:`l_2`
error quite easily as well:

.. code:: ipython3

    def l2_error(input_, target):
        return torch.linalg.norm(input_-target, ord=2)/torch.linalg.norm(input_, ord=2)
    
    
    print(f'l2 error: {l2_error(input_data[0, 0, :, -1], output[0, 0, :, -1]):.2%}')


.. parsed-literal::

    l2 error: 4.32%


More or less :math:`4\%` in :math:`l_2` error, which is really low
considering the fact that we use just **one** convolutional layer and a
simple feedforward to decrease the dimension. Let’s see now some
peculiarity of the filter.

Filter for upsampling
~~~~~~~~~~~~~~~~~~~~~

Suppose we have already the hidden dimension and we want to upsample on
a differen grid with more points. Let’s see how to do it:

.. code:: ipython3

    # setting the seed
    torch.manual_seed(seed)
    
    grid2 = circle_grid(1500) # triple number of points
    input_data2 = torch.zeros(size=(1, 1, grid2.shape[0], 3))
    input_data2[0, 0, :, :-1] = grid2
    input_data2[0, 0, :, -1] = torch.sin(pi *
                                        grid2[:, 0]) * torch.sin(pi * grid2[:, 1])
    
    # get the hidden dimension representation from original input
    latent = net.encoder(input_data)
    
    # upsample on the second input_data2
    output = net.decoder(latent, input_data2).detach()
    
    # show the picture
    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))
    pic1 = axes[0].scatter(grid2[:, 0], grid2[:, 1], c=input_data2[0, 0, :, -1])
    axes[0].set_title("Real")
    fig.colorbar(pic1)
    plt.subplot(1, 2, 2)
    pic2 = axes[1].scatter(grid2[:, 0], grid2[:, 1], c=output[0, 0, :, -1])
    axes[1].set_title("Up-sampling")
    fig.colorbar(pic2)
    plt.tight_layout()
    plt.show()




.. image:: tutorial_files/tutorial_45_0.png


As we can see we have a very good approximation of the original
function, even thought some noise is present. Let’s calculate the error
now:

.. code:: ipython3

    print(f'l2 error: {l2_error(input_data2[0, 0, :, -1], output[0, 0, :, -1]):.2%}')


.. parsed-literal::

    l2 error: 8.49%


Autoencoding at different resolution
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

In the previous example we already had the hidden dimension (of original
input) and we used it to upsample. Sometimes however we have a more fine
mesh solution and we simply want to encode it. This can be done without
retraining! This procedure can be useful in case we have many points in
the mesh and just a smaller part of them are needed for training. Let’s
see the results of this:

.. code:: ipython3

    # setting the seed
    torch.manual_seed(seed)
    
    grid2 = circle_grid(3500)  # very fine mesh
    input_data2 = torch.zeros(size=(1, 1, grid2.shape[0], 3))
    input_data2[0, 0, :, :-1] = grid2
    input_data2[0, 0, :, -1] = torch.sin(pi *
                                         grid2[:, 0]) * torch.sin(pi * grid2[:, 1])
    
    # get the hidden dimension representation from more fine mesh input
    latent = net.encoder(input_data2)
    
    # upsample on the second input_data2
    output = net.decoder(latent, input_data2).detach()
    
    # show the picture
    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))
    pic1 = axes[0].scatter(grid2[:, 0], grid2[:, 1], c=input_data2[0, 0, :, -1])
    axes[0].set_title("Real")
    fig.colorbar(pic1)
    plt.subplot(1, 2, 2)
    pic2 = axes[1].scatter(grid2[:, 0], grid2[:, 1], c=output[0, 0, :, -1])
    axes[1].set_title("Autoencoder not re-trained")
    fig.colorbar(pic2)
    plt.tight_layout()
    plt.show()
    
    # calculate l2 error
    print(
        f'l2 error: {l2_error(input_data2[0, 0, :, -1], output[0, 0, :, -1]):.2%}')




.. image:: tutorial_files/tutorial_49_0.png


.. parsed-literal::

    l2 error: 8.59%


What’s next?
------------

We have shown the basic usage of a convolutional filter. There are
additional extensions possible:

1. Train using Physics Informed strategies

2. Use the filter to build an unstructured convolutional autoencoder for
   reduced order modelling

3. Many more…
