Join: feature dataset

src/continuity/benchmarks/sine.py

@@ -1,8 +1,7 @@
 """Sine benchmark."""
 
 from continuity.benchmarks import Benchmark
-from continuity.data import split
-from continuity.data.datasets import Sine
+from continuity.data import Sine, split
 from continuity.operators.losses import Loss, MSELoss
 from torch.utils.data import Dataset
 

src/continuity/data/__init__.py

@@ -8,6 +8,22 @@
 import os
 import torch
 
+from .dataset import OperatorDataset, SelfSupervisedOperatorDataset
+from .shape import DatasetShapes
+from .sine import Sine
+from .flame import Flame, FlameDataLoader
+
+__all__ = [
+    "OperatorDataset",
+    "SelfSupervisedOperatorDataset",
+    "DatasetShapes",
+    "Sine",
+    "Flame",
+    "FlameDataLoader",
+    "device",
+    "split",
+]
+
 
 def get_device() -> torch.device:
     """Get torch device.
@@ -34,11 +50,6 @@ def get_device() -> torch.device:
 device = get_device()
 
 
-def tensor(x):
-    """Default conversion for tensors."""
-    return torch.tensor(x, dtype=torch.float32)
-
-
 def split(dataset, split=0.5, seed=None):
     """
     Split data set into two parts.
@@ -70,7 +81,7 @@ def dataset_loss(dataset, operator, loss_fn):
     loss = 0.0
 
     for x, u, y, v in dataset:
-        batch_size = x.shape[0]
-        loss += loss_fn(operator, x, u, y, v) / batch_size
+        x, u, y, v = x.unsqueeze(0), u.unsqueeze(0), y.unsqueeze(0), v.unsqueeze(0)
+        loss += loss_fn(operator, x, u, y, v)
 
     return loss

src/continuity/data/dataset.py

@@ -0,0 +1,161 @@
+"""
+`continuity.data`
+
+Data sets in Continuity.
+Every data set is a list of `(x, u, y, v)` tuples.
+"""
+
+import torch
+import torch.utils.data as td
+from typing import Tuple
+
+from .shape import DatasetShapes, TensorShape
+
+
+class OperatorDataset(td.Dataset):
+    """A dataset for operator training.
+
+    In operator training, at least one function is mapped onto a second one. To fulfill the properties discretization
+    invariance, domain independence and learn operators with physics-based loss access to at least four different
+    discretized spaces is necessary. One on which the input is sampled (x), the input function sampled on these points
+    (u), the discretization of the output space (y), and the output of the operator (v) sampled on these points. Not
+    all loss functions and/or operators need access to all of these attributes.
+
+    Args:
+        x: Tensor of shape (#observations, #sensors, x-dim) with sensor positions.
+        u: Tensor of shape (#observations, #sensors, u-dim) with evaluations of the input functions at sensor positions.
+        y: Tensor of shape (#observations, #evaluations, y-dim) with evaluation positions.
+        v: Tensor of shape (#observations, #evaluations, v-dim) with ground truth operator mappings.
+
+    Attributes:
+        shapes (dataclass): Shape of all tensors.
+        transform (dict): Transformations for each tensor.
+    """
+
+    def __init__(
+        self,
+        x: torch.Tensor,
+        u: torch.Tensor,
+        y: torch.Tensor,
+        v: torch.Tensor,
+        x_transform=None,
+        u_transform=None,
+        y_transform=None,
+        v_transform=None,
+    ):
+        assert x.ndim == u.ndim == y.ndim == v.ndim == 3, "Wrong number of dimensions."
+        assert (
+            x.size(0) == u.size(0) == y.size(0) == v.size(0)
+        ), "Inconsistent number of observations."
+        assert x.size(1) == u.size(1), "Inconsistent number of sensors."
+        assert y.size(1) == v.size(1), "Inconsistent number of evaluations."
+
+        super().__init__()
+
+        self.x = x
+        self.u = u
+        self.y = y
+        self.v = v
+
+        # used to initialize architectures
+        self.shapes = DatasetShapes(
+            num_observations=int(x.size(0)),
+            x=TensorShape(*x.size()[1:]),
+            u=TensorShape(*u.size()[1:]),
+            y=TensorShape(*y.size()[1:]),
+            v=TensorShape(*v.size()[1:]),
+        )
+
+        self.transform = {
+            dim: tf
+            for dim, tf in [
+                ("x", x_transform),
+                ("u", u_transform),
+                ("y", y_transform),
+                ("v", v_transform),
+            ]
+            if tf is not None
+        }
+
+    def __len__(self) -> int:
+        """Return the number of samples.
+
+        Returns:
+            number of samples in the entire set.
+        """
+        return self.shapes.num_observations
+
+    def __getitem__(
+        self, idx
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Retrieves the input-output pair at the specified index and applies transformations.
+
+        Parameters:
+            - idx: The index of the sample to retrieve.
+
+        Returns:
+            A tuple containing the three input tensors and the output tensor for the given index.
+        """
+        sample = {
+            "x": self.x[idx],
+            "u": self.u[idx],
+            "y": self.y[idx],
+            "v": self.v[idx],
+        }
+
+        # transform
+        for dim, val in sample.items():
+            if dim in self.transform:
+                sample[dim] = self.transform[dim](val)
+
+        return sample["x"], sample["u"], sample["y"], sample["v"]
+
+
+class SelfSupervisedOperatorDataset(OperatorDataset):
+    """
+    A `SelfSupervisedOperatorDataset` is a data set that contains data for self-supervised learning.
+    Every data point is created by taking one sensor as a label.
+
+    Every observation consists of tuples `(x, u, y, v)`, where `x` contains the sensor
+    positions, `u` the sensor values, and `y = x_i` and `v = u_i` are
+    the label's coordinate its value for all `i`.
+
+    Args:
+        x: Sensor positions of shape (num_observations, num_sensors, coordinate_dim)
+        u: Sensor values of shape (num_observations, num_sensors, num_channels)
+    """
+
+    def __init__(self, x: torch.Tensor, u: torch.Tensor):
+        self.num_observations = u.shape[0]
+        self.num_sensors = u.shape[1]
+        self.coordinate_dim = x.shape[-1]
+        self.num_channels = u.shape[-1]
+
+        # Check consistency across observations
+        for i in range(self.num_observations):
+            assert (
+                x[i].shape[-1] == self.coordinate_dim
+            ), "Inconsistent coordinate dimension."
+            assert (
+                u[i].shape[-1] == self.num_channels
+            ), "Inconsistent number of channels."
+
+        xs, us, ys, vs = [], [], [], []
+
+        for i in range(self.num_observations):
+            # Add one data point for every sensor
+            for j in range(self.num_sensors):
+                y = x[i][j].unsqueeze(0)
+                v = u[i][j].unsqueeze(0)
+
+                xs.append(x[i])
+                us.append(u[i])
+                ys.append(y)
+                vs.append(v)
+
+        xs = torch.stack(xs)
+        us = torch.stack(us)
+        ys = torch.stack(ys)
+        vs = torch.stack(vs)
+
+        super().__init__(xs, us, ys, vs)