Transform helpers cleanup

.github/workflows/pytest.yml

@@ -0,0 +1,28 @@
+name: pytest
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.8", "3.8", "3.9", "3.10", "3.11", "3.12"]
+
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: |
+          pip install uv
+          uv pip install --system -e ".[dev,examples]"
+      - name: Test with pytest
+        run: |
+          pytest

pyproject.toml

@@ -42,6 +42,8 @@ dev = [
     "pyright>=1.1.308",
     "ruff==0.6.2",
     "pre-commit==3.3.2",
+    "pytest",
+    "hypothesis[numpy]",
 ]
 examples = [
     "torch>=1.13.1",

src/viser/transforms/_base.py

@@ -9,9 +9,6 @@
 class MatrixLieGroup(abc.ABC):
     """Interface definition for matrix Lie groups."""
 
-    # Class properties.
-    # > These will be set in `_utils.register_lie_group()`.
-
     matrix_dim: ClassVar[int]
     """Dimension of square matrix output from `.as_matrix()`."""
 
@@ -36,6 +33,19 @@ def __init__(
         """Construct a group object from its underlying parameters."""
         raise NotImplementedError()
 
+    def __init_subclass__(
+        cls,
+        matrix_dim: int = 0,
+        parameters_dim: int = 0,
+        tangent_dim: int = 0,
+        space_dim: int = 0,
+    ) -> None:
+        """Set class properties for subclasses. We default to dummy values."""
+        cls.matrix_dim = matrix_dim
+        cls.parameters_dim = parameters_dim
+        cls.tangent_dim = tangent_dim
+        cls.space_dim = space_dim
+
     # Shared implementations.
 
     @overload
@@ -66,11 +76,14 @@ def __matmul__(
 
     @classmethod
     @abc.abstractmethod
-    def identity(cls, batch_axes: Tuple[int, ...] = ()) -> Self:
+    def identity(
+        cls, batch_axes: Tuple[int, ...] = (), dtype: onpt.DTypeLike = onp.float64
+    ) -> Self:
         """Returns identity element.
 
         Args:
             batch_axes: Any leading batch axes for the output transform.
+            dtype: Datatype for the output.
 
         Returns:
             Identity element.
@@ -172,20 +185,25 @@ def normalize(self) -> Self:
             Normalized group member.
         """
 
-    # @classmethod
-    # @abc.abstractmethod
-    # def sample_uniform(cls, key: onp.ndarray, batch_axes: Tuple[int, ...] = ()) -> Self:
-    #     """Draw a uniform sample from the group. Translations (if applicable) are in the
-    #     range [-1, 1].
-    #
-    #     Args:
-    #         key: PRNG key, as returned by `jax.random.PRNGKey()`.
-    #         batch_axes: Any leading batch axes for the output transforms. Each
-    #             sampled transform will be different.
-    #
-    #     Returns:
-    #         Sampled group member.
-    #     """
+    @classmethod
+    @abc.abstractmethod
+    def sample_uniform(
+        cls,
+        rng: onp.random.Generator,
+        batch_axes: Tuple[int, ...] = (),
+        dtype: onpt.DTypeLike = onp.float64,
+    ) -> Self:
+        """Draw a uniform sample from the group. Translations (if applicable) are in the
+        range [-1, 1].
+
+        Args:
+            rng: numpy generator object.
+            batch_axes: Any leading batch axes for the output transforms. Each
+                sampled transform will be different.
+
+        Returns:
+            Sampled group member.
+        """
 
     @final
     def get_batch_axes(self) -> Tuple[int, ...]:

src/viser/transforms/_se2.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import dataclasses
 from typing import Tuple, cast
 
@@ -7,17 +9,17 @@
 
 from . import _base, hints
 from ._so2 import SO2
-from .utils import broadcast_leading_axes, get_epsilon, register_lie_group
+from .utils import broadcast_leading_axes, get_epsilon
 
 
-@register_lie_group(
+@dataclasses.dataclass(frozen=True)
+class SE2(
+    _base.SEBase[SO2],
     matrix_dim=3,
     parameters_dim=4,
     tangent_dim=3,
     space_dim=2,
-)
-@dataclasses.dataclass(frozen=True)
-class SE2(_base.SEBase[SO2]):
+):
     """Special Euclidean group for proper rigid transforms in 2D. Broadcasting
     rules are the same as for numpy.
 
@@ -39,7 +41,7 @@ def __repr__(self) -> str:
         return f"{self.__class__.__name__}(unit_complex={unit_complex}, xy={xy})"
 
     @staticmethod
-    def from_xy_theta(x: hints.Scalar, y: hints.Scalar, theta: hints.Scalar) -> "SE2":
+    def from_xy_theta(x: hints.Scalar, y: hints.Scalar, theta: hints.Scalar) -> SE2:
         """Construct a transformation from standard 2D pose parameters.
 
         This is not the same as integrating over a length-3 twist.
@@ -56,7 +58,7 @@ def from_rotation_and_translation(
         cls,
         rotation: SO2,
         translation: onpt.NDArray[onp.floating],
-    ) -> "SE2":
+    ) -> SE2:
         assert translation.shape[-1:] == (2,)
         rotation, translation = broadcast_leading_axes((rotation, translation))
         return SE2(
@@ -77,16 +79,18 @@ def translation(self) -> onpt.NDArray[onp.floating]:
 
     @classmethod
     @override
-    def identity(cls, batch_axes: Tuple[int, ...] = ()) -> "SE2":
+    def identity(
+        cls, batch_axes: Tuple[int, ...] = (), dtype: onpt.DTypeLike = onp.float64
+    ) -> SE2:
         return SE2(
             unit_complex_xy=onp.broadcast_to(
-                onp.array([1.0, 0.0, 0.0, 0.0]), (*batch_axes, 4)
+                onp.array([1.0, 0.0, 0.0, 0.0], dtype=dtype), (*batch_axes, 4)
             )
         )
 
     @classmethod
     @override
-    def from_matrix(cls, matrix: onpt.NDArray[onp.floating]) -> "SE2":
+    def from_matrix(cls, matrix: onpt.NDArray[onp.floating]) -> SE2:
         assert matrix.shape[-2:] == (3, 3) or matrix.shape[-2:] == (2, 3)
         # Currently assumes bottom row is [0, 0, 1].
         return SE2.from_rotation_and_translation(
@@ -123,7 +127,7 @@ def as_matrix(self) -> onpt.NDArray[onp.floating]:
 
     @classmethod
     @override
-    def exp(cls, tangent: onpt.NDArray[onp.floating]) -> "SE2":
+    def exp(cls, tangent: onpt.NDArray[onp.floating]) -> SE2:
         # Reference:
         # > https://github.com/strasdat/Sophus/blob/a0fe89a323e20c42d3cecb590937eb7a06b8343a/sophus/se2.hpp#L558
         # Also see:
@@ -146,21 +150,15 @@ def exp(cls, tangent: onpt.NDArray[onp.floating]) -> "SE2":
         )
 
         theta_sq = theta**2
-        sin_over_theta = cast(
-            onp.ndarray,
-            onp.where(
-                use_taylor,
-                1.0 - theta_sq / 6.0,
-                onp.sin(safe_theta) / safe_theta,
-            ),
+        sin_over_theta = onp.where(
+            use_taylor,
+            1.0 - theta_sq / 6.0,
+            onp.sin(safe_theta) / safe_theta,
         )
-        one_minus_cos_over_theta = cast(
-            onp.ndarray,
-            onp.where(
-                use_taylor,
-                0.5 * theta - theta * theta_sq / 24.0,
-                (1.0 - onp.cos(safe_theta)) / safe_theta,
-            ),
+        one_minus_cos_over_theta = onp.where(
+            use_taylor,
+            0.5 * theta - theta * theta_sq / 24.0,
+            (1.0 - onp.cos(safe_theta)) / safe_theta,
         )
 
         V = onp.stack(
@@ -172,9 +170,12 @@ def exp(cls, tangent: onpt.NDArray[onp.floating]) -> "SE2":
             ],
             axis=-1,
         ).reshape((*tangent.shape[:-1], 2, 2))
+
         return SE2.from_rotation_and_translation(
             rotation=SO2.from_radians(theta),
-            translation=onp.einsum("...ij,...j->...i", V, tangent[..., :2]),
+            translation=onp.einsum("...ij,...j->...i", V, tangent[..., :2]).astype(
+                tangent.dtype
+            ),
         )
 
     @override
@@ -224,10 +225,10 @@ def log(self) -> onpt.NDArray[onp.floating]:
             ],
             axis=-1,
         )
-        return tangent
+        return tangent.astype(self.unit_complex_xy.dtype)
 
     @override
-    def adjoint(self: "SE2") -> onpt.NDArray[onp.floating]:
+    def adjoint(self: SE2) -> onpt.NDArray[onp.floating]:
         cos, sin, x, y = onp.moveaxis(self.unit_complex_xy, -1, 0)
         return onp.stack(
             [
@@ -244,21 +245,15 @@ def adjoint(self: "SE2") -> onpt.NDArray[onp.floating]:
             axis=-1,
         ).reshape((*self.get_batch_axes(), 3, 3))
 
-    # @classmethod
-    # @override
-    # def sample_uniform(
-    #     cls, key: onp.ndarray, batch_axes: jdc.Static[Tuple[int, ...]] = ()
-    # ) -> "SE2":
-    #     key0, key1 = jax.random.split(key)
-    #     return SE2.from_rotation_and_translation(
-    #         rotation=SO2.sample_uniform(key0, batch_axes=batch_axes),
-    #         translation=jax.random.uniform(
-    #             key=key1,
-    #             shape=(
-    #                 *batch_axes,
-    #                 2,
-    #             ),
-    #             minval=-1.0,
-    #             maxval=1.0,
-    #         ),
-    #     )
+    @classmethod
+    @override
+    def sample_uniform(
+        cls,
+        rng: onp.random.Generator,
+        batch_axes: Tuple[int, ...] = (),
+        dtype: onpt.DTypeLike = onp.float64,
+    ) -> SE2:
+        return SE2.from_rotation_and_translation(
+            SO2.sample_uniform(rng, batch_axes=batch_axes, dtype=dtype),
+            rng.uniform(low=-1.0, high=1.0, size=(*batch_axes, 2)).astype(dtype),
+        )