diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 36828cf..377319c 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -187,9 +187,9 @@ Before opening a pull request that changes **`physicsnemo/cfd/evaluation/`** or pytest -q test/ci_tests/ ``` -The **`test_evaluation.py`** module may skip entirely if -`physicsnemo.utils.sdf` is unavailable (install or upgrade -`nvidia-physicsnemo`). +The **`compute_error_vs_sdf`** tests in **`test_l2_errors.py`** skip cleanly +when **`warp`** is unavailable (install via the **`gpu`** optional extra in +[`pyproject.toml`](pyproject.toml)). Do **not** commit generated benchmark outputs under **`workflows/benchmarking/`** (e.g. `benchmark_results*/`, `gpu_output.log`, diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..3c31716 --- /dev/null +++ b/uv.lock @@ -0,0 +1,4067 @@ +version = 1 +revision = 3 +requires-python = ">=3.12" +resolution-markers = [ + "python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform == 'win32'", + "python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform == 'win32'", + "python_full_version < '3.13' and platform_machine != 's390x' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform == 'emscripten'", + "python_full_version < '3.13' and platform_machine 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""" -from typing import Literal, Sequence +from typing import Sequence import numpy as np import pyvista as pv @@ -30,15 +30,85 @@ from cuml.neighbors import NearestNeighbors from torch.utils.data import Dataset -from physicsnemo.utils.domino.utils import ( +from physicsnemo.models.domino.utils import ( calculate_center_of_mass, create_grid, normalize, ) -from physicsnemo.utils.sdf import signed_distance_field +from physicsnemo.nn.functional import signed_distance_field import torch +### PhysicsNeMo v2 made several DoMINO utilities torch-only. The rest of this +### datapipe is numpy-native (it feeds cuml's NearestNeighbors and uses +### ``np.array`` / ``rng.rand`` extensively), so we keep two small numpy-in / +### numpy-out wrappers below to isolate the torch hop at the boundary. + + +def _compute_sdf_np( + mesh_vertices: np.ndarray, + mesh_indices: np.ndarray, + input_points: np.ndarray, + use_sign_winding_number: bool = True, +) -> tuple[np.ndarray, np.ndarray]: + """Compute signed distance + closest hit points from a triangle mesh. + + Thin numpy-in / numpy-out wrapper around the v2 + ``physicsnemo.nn.functional.signed_distance_field`` op, which is itself + torch-only and always returns ``(sdf, hit_points)`` (no + ``include_hit_points`` kwarg). + + Args: + mesh_vertices: Mesh vertex coordinates, shape ``(n_vertices, 3)``. + mesh_indices: Triangle connectivity as flattened indices, shape + ``(3 * n_faces,)``; or face triplets, shape ``(n_faces, 3)``. + input_points: Query points at which to evaluate the SDF, shape + ``(..., 3)``. + use_sign_winding_number: Use the winding-number sign convention + (works on non-watertight meshes). Defaults to ``True``, matching + the prior behavior of every call site in this datapipe. + + Returns: + Tuple ``(sdf, hit_points)`` of numpy arrays. ``sdf`` has shape + ``input_points.shape[:-1]`` and ``hit_points`` has shape + ``input_points.shape``. Returned on CPU regardless of input device. + """ + sdf, hit_points = signed_distance_field( + mesh_vertices=torch.as_tensor(mesh_vertices), + mesh_indices=torch.as_tensor(mesh_indices), + input_points=torch.as_tensor(input_points), + use_sign_winding_number=use_sign_winding_number, + ) + return sdf.cpu().numpy(), hit_points.cpu().numpy() + + +def _create_grid_np( + max_coords: np.ndarray, + min_coords: np.ndarray, + resolution: Sequence[int], +) -> np.ndarray: + """Build a regular 3D grid via the v2 ``create_grid`` helper. + + The v2 ``physicsnemo.models.domino.utils.create_grid`` is torch-typed (it + reads ``.dtype`` and ``.device`` off its first arg), so we wrap it for + numpy callers. Output dtype is float32 to match the rest of the datapipe. + + Args: + max_coords: Upper bounds ``[x_max, y_max, z_max]``, shape ``(3,)``. + min_coords: Lower bounds ``[x_min, y_min, z_min]``, shape ``(3,)``. + resolution: Number of grid points along each axis, length 3. + + Returns: + Grid coordinates of shape ``(nx, ny, nz, 3)`` as ``np.float32``. + """ + grid_t = create_grid( + torch.as_tensor(np.asarray(max_coords), dtype=torch.float32), + torch.as_tensor(np.asarray(min_coords), dtype=torch.float32), + torch.as_tensor(np.asarray(resolution), dtype=torch.int32), + ) + return grid_t.cpu().numpy() + + class DesignDatapipe(Dataset): """PyTorch Dataset for processing surface meshes into DoMINO inputs.""" @@ -79,9 +149,6 @@ def __init__( # Initialize random number generator, for reproducibility rng = np.random.RandomState(seed) - # Initialize the output dictionary, which will store all data for the datapipe - out_dict: dict[str, np.ndarray] = {} - ### First, do computation that is required for all model_types length_scale = np.amax(self.mesh.points, 0) - np.amin(self.mesh.points, 0) stl_centers = self.mesh.cell_centers().points @@ -110,28 +177,21 @@ def __init__( v_min = np.asarray(bounding_box[0]) nx, ny, nz = grid_resolution - grid = create_grid(v_max, v_min, grid_resolution) + grid = _create_grid_np(v_max, v_min, grid_resolution) grid_reshaped = grid.reshape(nx * ny * nz, 3) - # SDF on grid - sdf_grid = signed_distance_field( - mesh_vertices=mesh.points, - mesh_indices=mesh_indices_flattened, - input_points=grid_reshaped, - use_sign_winding_number=True, + sdf_grid, _ = _compute_sdf_np( + mesh.points, mesh_indices_flattened, grid_reshaped ) - sdf_grid = np.array(sdf_grid).reshape(nx, ny, nz) + sdf_grid = sdf_grid.reshape(nx, ny, nz) - surf_grid = create_grid(s_max, s_min, grid_resolution) + surf_grid = _create_grid_np(s_max, s_min, grid_resolution) surf_grid_reshaped = surf_grid.reshape(nx * ny * nz, 3) - sdf_surf_grid = signed_distance_field( - mesh_vertices=mesh.points, - mesh_indices=mesh_indices_flattened, - input_points=surf_grid_reshaped, - use_sign_winding_number=True, + sdf_surf_grid, _ = _compute_sdf_np( + mesh.points, mesh_indices_flattened, surf_grid_reshaped ) - sdf_surf_grid = np.array(sdf_surf_grid).reshape(nx, ny, nz) + sdf_surf_grid = sdf_surf_grid.reshape(nx, ny, nz) # Sample surface_vertices grid = normalize(grid, v_max, v_min) @@ -163,20 +223,15 @@ def __init__( # Volume processing volume_coordinates = (v_max - v_min) * rng.rand(1000, 3) + v_min - sdf_nodes, sdf_node_closest_point = signed_distance_field( - mesh.points, - mesh_indices_flattened, - volume_coordinates, - include_hit_points=True, - use_sign_winding_number=True, + sdf_nodes, sdf_node_closest_point = _compute_sdf_np( + mesh.points, mesh_indices_flattened, volume_coordinates ) - sdf_nodes = np.array(sdf_nodes).reshape(-1, 1) - sdf_node_closest_point = np.array(sdf_node_closest_point) + sdf_nodes = sdf_nodes.reshape(-1, 1) pos_normals_closest = volume_coordinates - sdf_node_closest_point pos_volume_center_of_mass = volume_coordinates - center_of_mass volume_coordinates = normalize(volume_coordinates, v_max, v_min) - vol_grid_max_min = np.float32(np.asarray([v_min, v_max])) - surf_grid_max_min = np.float32(np.asarray([s_min, s_max])) + vol_grid_max_min = np.asarray([v_min, v_max], dtype=np.float32) + surf_grid_max_min = np.asarray([s_min, s_max], dtype=np.float32) self.out_dict = dict( pos_volume_closest=pos_normals_closest, diff --git a/workflows/domino_design_sensitivities/main.py b/workflows/domino_design_sensitivities/main.py index 1eb1330..059d8d5 100644 --- a/workflows/domino_design_sensitivities/main.py +++ b/workflows/domino_design_sensitivities/main.py @@ -33,7 +33,7 @@ import torch from physicsnemo.models.domino.model import DoMINO -from physicsnemo.utils.domino.utils import unnormalize +from physicsnemo.models.domino.utils import unnormalize from torch.nn.parallel import DistributedDataParallel from physicsnemo.distributed import DistributedManager