New Metric: Soft Dynamic Time Warping (Soft-DTW)

README.md

@@ -39,13 +39,15 @@ ______________________________________________________________________
 </div>
 
 # Looking for GPUs?
-Over 340,000 developers use [Lightning Cloud](https://lightning.ai/?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme) - purpose-built for PyTorch and PyTorch Lightning. 
-- [GPUs](https://lightning.ai/pricing?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme) from $0.19.   
-- [Clusters](https://lightning.ai/clusters?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme): frontier-grade training/inference clusters.   
+
+Over 340,000 developers use [Lightning Cloud](https://lightning.ai/?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme) - purpose-built for PyTorch and PyTorch Lightning.
+
+- [GPUs](https://lightning.ai/pricing?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme) from $0.19.
+- [Clusters](https://lightning.ai/clusters?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme): frontier-grade training/inference clusters.
 - [AI Studio (vibe train)](https://lightning.ai/studios?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme): workspaces where AI helps you debug, tune and vibe train.
-- [AI Studio (vibe deploy)](https://lightning.ai/studios?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme): workspaces where AI helps you optimize, and deploy models.     
+- [AI Studio (vibe deploy)](https://lightning.ai/studios?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme): workspaces where AI helps you optimize, and deploy models.
 - [Notebooks](https://lightning.ai/notebooks?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme): Persistent GPU workspaces where AI helps you code and analyze.
-- [Inference](https://lightning.ai/deploy?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme): Deploy models as inference APIs.   
+- [Inference](https://lightning.ai/deploy?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme): Deploy models as inference APIs.
 
 # Installation
 

docs/source/index.rst

@@ -253,6 +253,14 @@ Or directly from conda
    :caption: Video
    :glob:
 
+   timeseries/*
+
+.. toctree::
+   :maxdepth: 2
+   :name: timeseries
+   :caption: Time Series
+   :glob:
+
    video/*
 
 .. toctree::

docs/source/links.rst

@@ -184,3 +184,4 @@
 .. _Deep Image Structure and Texture Similarity: https://arxiv.org/abs/2004.07728
 .. _KonIQ-10k: https://database.mmsp-kn.de/koniq-10k-database.html
 .. _KADID-10k: https://database.mmsp-kn.de/kadid-10k-database.html
+.. _SoftDTW: https://arxiv.org/abs/1703.01541

docs/source/timeseries/softdtw.rst

@@ -0,0 +1,22 @@
+.. customcarditem::
+   :header: Soft Dynamic Time Warping
+   :image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/tabular_classification.svg
+   :tags: timeseries
+
+.. include:: ../links.rst
+
+#########################
+Soft Dynamic Time Warping
+#########################
+
+Module Interface
+________________
+
+.. autoclass:: torchmetrics.timeseries.SoftDTW
+    :exclude-members: update, compute
+
+
+Functional Interface
+____________________
+
+.. autofunction:: torchmetrics.functional.timeseries.soft_dtw

requirements/timeseries_test.txt

@@ -0,0 +1 @@
+pysdtw==0.0.5

src/torchmetrics/functional/timeseries/__init__.py

@@ -0,0 +1,16 @@
+# Copyright The Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from torchmetrics.functional.timeseries.softdtw import soft_dtw
+
+__all__ = ["soft_dtw"]

src/torchmetrics/functional/timeseries/softdtw.py

@@ -0,0 +1,163 @@
+# Copyright The Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import math
+from typing import Callable, Literal, Optional
+
+import torch
+from torch import Tensor
+
+
+def _soft_dtw_validate_args(
+    preds: Tensor, target: Tensor, gamma: float, reduction: Literal["mean", "sum", "none"]
+) -> None:
+    """Validate the input arguments for the soft_dtw function."""
+    valid_reduction = ("mean", "sum", "none")
+    if reduction not in valid_reduction:
+        raise ValueError(f"Argument `reduction` must be one of {valid_reduction}, but got {reduction}")
+    if preds.ndim != 3 or target.ndim != 3:
+        raise ValueError("Inputs preds and target must be 3-dimensional tensors of shape [B, N, D] and [B, M, D].")
+    if preds.shape[0] != target.shape[0]:
+        raise ValueError("Batch size of preds and target must be the same.")
+    if preds.shape[2] != target.shape[2]:
+        raise ValueError("Feature dimension of preds and target must be the same.")
+    if not isinstance(gamma, float) or gamma <= 0:
+        raise ValueError("Gamma must be a positive float.")
+
+
+def _soft_dtw_update(preds: Tensor, target: Tensor, gamma: float, distance_fn: Optional[Callable] = None) -> Tensor:
+    """Compute the Soft-DTW distance between two batched sequences."""
+    b, n, d = preds.shape
+    _, m, _ = target.shape
+    device, dtype = target.device, target.dtype
+    if preds.dtype != target.dtype:
+        target = target.to(preds.dtype)
+
+    if distance_fn is None:
+
+        def distance_fn(x: Tensor, y: Tensor) -> Tensor:
+            """Default to squared Euclidean distance."""
+            return torch.cdist(x, y, p=2).pow(2)
+
+    distances = distance_fn(preds, target)  # [B, N, M]
+
+    r = torch.ones((b, n + 2, m + 2), device=device, dtype=dtype) * math.inf
+    r[:, 0, 0] = 0.0
+
+    def softmin(a: Tensor, b: Tensor, c: Tensor, gamma: float) -> Tensor:
+        """Compute the soft minimum of three tensors."""
+        vals = torch.stack([a, b, c], dim=-1)
+        return -gamma * torch.logsumexp(-vals / gamma, dim=-1)
+
+    # Anti-diagonal approach inspired from https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8400444
+    for k in range(2, n + m + 1):
+        i_vals = torch.arange(1, n + 1, device=device)
+        j_vals = k - i_vals
+        mask = (j_vals >= 1) & (j_vals <= m)
+        i_vals = i_vals[mask]
+        j_vals = j_vals[mask]
+
+        if len(i_vals) == 0:
+            continue
+
+        r1 = r[:, i_vals - 1, j_vals - 1]
+        r2 = r[:, i_vals - 1, j_vals]
+        r3 = r[:, i_vals, j_vals - 1]
+        r[:, i_vals, j_vals] = distances[:, i_vals - 1, j_vals - 1] + softmin(r1, r2, r3, gamma)
+
+    return r[:, n, m]
+
+
+def _soft_dtw_compute(scores: Tensor, reduction: Literal["sum", "mean", "none"] = "mean") -> Tensor:
+    """Aggregate the computed Soft-DTW distances based on the specified reduction method."""
+    if reduction == "none":
+        return scores
+    if reduction == "mean":
+        return scores.mean()
+    return scores.sum()
+
+
+def soft_dtw(
+    preds: Tensor,
+    target: Tensor,
+    gamma: float = 1.0,
+    distance_fn: Optional[Callable] = None,
+    reduction: Literal["sum", "mean", "none"] = "mean",
+) -> Tensor:
+    r"""Compute the Soft Dynamic Time Warping (Soft-DTW) distance between two batched sequences.
+
+    This is a differentiable relaxation of the classic Dynamic Time Warping (DTW) algorithm, introduced by
+    Marco Cuturi and Mathieu Blondel (2017).
+    It replaces the hard minimum in DTW recursion with a soft-minimum using a log-sum-exp formulation:
+
+    .. math::
+        \text{softmin}_\gamma(a,b,c) = -\gamma \log \left( e^{-a/\gamma} + e^{-b/\gamma} + e^{-c/\gamma} \right)
+
+    The Soft-DTW recurrence is then defined as:
+
+    .. math::
+        R_{i,j} = D_{i,j} + \text{softmin}_\gamma(R_{i-1,j}, R_{i,j-1}, R_{i-1,j-1})
+
+    where :math:`D_{i,j}` is the pairwise distance between sequence elements :math:`x_i` and :math:`y_j`. It could be
+    computed using any differentiable distance function, such as squared Euclidean distance or cosine distance.
+
+    The final Soft-DTW distance is :math:`R_{N,M}`.
+
+    Args:
+        preds: Tensor of shape ``[B, N, D]`` — batch of input sequences.
+        target: Tensor of shape ``[B, M, D]`` — batch of target sequences.
+        gamma: Smoothing parameter (:math:`\gamma > 0`).
+            Smaller values make the loss closer to standard DTW (hard minimum),
+            while larger values produce a smoother and more differentiable surface.
+        distance_fn: Optional callable ``(x, y) -> [B, N, M]`` defining the pairwise distance matrix.
+            If ``None``, defaults to squared Euclidean distance.
+        reduction: indicates how to reduce over the batch dimension. Choose between [``sum``, ``mean``, ``none``].
+            Defaults to ``mean``.
+
+    Returns:
+        A tensor of shape ``[B]`` containing the Soft-DTW distance for each sequence pair in the batch.
+
+    Raises:
+        ValueError:
+            If ``reduction`` is not one of [``sum``, ``mean``, ``none``].
+        ValueError:
+            If ``gamma`` is not a positive float.
+        ValueError:
+            If input tensors to ``preds`` and ``target`` are not 3-dimensional
+            with the same batch size and feature dimension.
+
+    Example::
+        >>> import torch
+        >>> from torchmetrics.functional.timeseries import soft_dtw
+        >>>
+        >>> x = torch.tensor([[[0.0], [1.0], [2.0]]])  # [B, N, D]
+        >>> y = torch.tensor([[[0.0], [2.0], [3.0]]])  # [B, M, D]
+        >>> soft_dtw(x, y, gamma=0.1)
+        tensor([0.4003])
+
+
+    Example (custom distance function)::
+        >>> def cosine_dist(a, b):
+        ...     a = torch.nn.functional.normalize(a, dim=-1)
+        ...     b = torch.nn.functional.normalize(b, dim=-1)
+        ...     return 1 - torch.bmm(a, b.transpose(1, 2))
+        >>>
+        >>> x = torch.randn(2, 5, 3)
+        >>> y = torch.randn(2, 6, 3)
+        >>> soft_dtw(x, y, gamma=0.5, distance_fn=cosine_dist)
+        tensor([2.8301, 3.0128])
+
+    """
+    _soft_dtw_validate_args(preds, target, gamma, reduction)
+    scores = _soft_dtw_update(preds, target, gamma, distance_fn)
+    return _soft_dtw_compute(scores, reduction)

src/torchmetrics/timeseries/__init__.py

@@ -0,0 +1,16 @@
+# Copyright The Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from torchmetrics.timeseries.softdtw import SoftDTW
+
+__all__ = ["SoftDTW"]