From bd746a08a1a7f1ef5ebe4d0cfc5d8e117d9f25ea Mon Sep 17 00:00:00 2001 From: Mohammad Yusuf Date: Sat, 18 Jul 2026 00:53:08 +0530 Subject: [PATCH] Add Bi-SCNN simplicial backbone --- .../outputs/biscnn/results.json | 5272 +++++++++++++++++ 2026_tdl_challenge/run_evaluation.ipynb | 4 +- configs/model/simplicial/biscnn.yaml | 46 + test/nn/backbones/simplicial/test_biscnn.py | 195 + test/pipeline/test_pipeline.py | 2 +- topobench/nn/backbones/simplicial/biscnn.py | 421 ++ 6 files changed, 5937 insertions(+), 3 deletions(-) create mode 100644 2026_tdl_challenge/outputs/biscnn/results.json create mode 100644 configs/model/simplicial/biscnn.yaml create mode 100644 test/nn/backbones/simplicial/test_biscnn.py create mode 100644 topobench/nn/backbones/simplicial/biscnn.py diff --git a/2026_tdl_challenge/outputs/biscnn/results.json b/2026_tdl_challenge/outputs/biscnn/results.json new file mode 100644 index 000000000..e2bf2c9d0 --- /dev/null +++ b/2026_tdl_challenge/outputs/biscnn/results.json @@ 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"test_mse_by_total_triangles": 24.3908203125 + }, + "h_lo__d_hi__pl_lo": { + "test_best_rerun_accuracy": null, + "test_best_rerun_mse": 13567.82421875, + "test_triangles_total_structural": 13185.0, + "test_mse_by_total_triangles": 1.0290348288775124 + }, + "h_lo__d_hi__pl_hi": { + "test_best_rerun_accuracy": null, + "test_best_rerun_mse": 18262.5859375, + "test_triangles_total_structural": 2967.0, + "test_mse_by_total_triangles": 6.1552362445230875 + }, + "h_mid__d_lo__pl_lo": { + "test_best_rerun_accuracy": null, + "test_best_rerun_mse": 6504.43798828125, + "test_triangles_total_structural": 7485.0, + "test_mse_by_total_triangles": 0.8689963912199399 + }, + "h_mid__d_lo__pl_hi": { + "test_best_rerun_accuracy": null, + "test_best_rerun_mse": 3280.07080078125, + "test_triangles_total_structural": 1158.0, + "test_mse_by_total_triangles": 2.8325309160459846 + }, + "h_mid__d_hi__pl_lo": { + "test_best_rerun_accuracy": null, + "test_best_rerun_mse": 13444.41015625, + "test_triangles_total_structural": 43064.0, + "test_mse_by_total_triangles": 0.3121960374384637 + }, + "h_mid__d_hi__pl_hi": { + "test_best_rerun_accuracy": null, + "test_best_rerun_mse": 7962.2431640625, + "test_triangles_total_structural": 14262.0, + "test_mse_by_total_triangles": 0.5582837725468027 + }, + "h_hi__d_lo__pl_lo": { + "test_best_rerun_accuracy": null, + "test_best_rerun_mse": 6843.642578125, + "test_triangles_total_structural": 19509.0, + "test_mse_by_total_triangles": 0.3507941246668204 + }, + "h_hi__d_lo__pl_hi": { + "test_best_rerun_accuracy": null, + "test_best_rerun_mse": 1604.6036376953125, + "test_triangles_total_structural": 5625.0, + "test_mse_by_total_triangles": 0.28526286892361113 + }, + "h_hi__d_hi__pl_lo": { + "test_best_rerun_accuracy": null, + "test_best_rerun_mse": 94704.640625, + "test_triangles_total_structural": 88403.0, + "test_mse_by_total_triangles": 1.0712831083221157 + } + }, + "output_dir": "E:\\PhD_Projects\\TopoBench\\logs\\train\\runs\\notebook_gu_grid_2026-07-17_17-17-51__triangle_counting__11__h_hi__d_hi__pl_hi__s44" + } + ] +} diff --git a/2026_tdl_challenge/run_evaluation.ipynb b/2026_tdl_challenge/run_evaluation.ipynb index 8542dbaab..8993bb2c4 100644 --- a/2026_tdl_challenge/run_evaluation.ipynb +++ b/2026_tdl_challenge/run_evaluation.ipynb @@ -98,13 +98,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "config_cell", "metadata": {}, "outputs": [], "source": [ "# Your model configuration (e.g., \"graph/gcn\", \"graph/gin\", \"graph/gat\")\n", - "MODEL_CONFIG = \"graph/gin\"" + "MODEL_CONFIG = \"simplicial/biscnn\"" ] }, { diff --git a/configs/model/simplicial/biscnn.yaml b/configs/model/simplicial/biscnn.yaml new file mode 100644 index 000000000..4451f052f --- /dev/null +++ b/configs/model/simplicial/biscnn.yaml @@ -0,0 +1,46 @@ +_target_: topobench.model.TBModel +model_name: biscNN +model_domain: simplicial + +feature_encoder: + _target_: topobench.nn.encoders.${model.feature_encoder.encoder_name} + encoder_name: AllCellFeatureEncoder + in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} + out_channels: 32 + proj_dropout: 0.0 + selected_dimensions: + - 0 + - 1 + - 2 + +backbone: + _target_: topobench.nn.backbones.simplicial.biscnn.BiSCNN + in_channels_all: + - ${model.feature_encoder.out_channels} + - ${model.feature_encoder.out_channels} + - ${model.feature_encoder.out_channels} + hidden_channels_all: + - ${model.feature_encoder.out_channels} + - ${model.feature_encoder.out_channels} + - ${model.feature_encoder.out_channels} + n_layers: 2 + sc_order: 3 + bias: false + +backbone_wrapper: + _target_: topobench.nn.wrappers.SCCNNWrapper + _partial_: true + wrapper_name: SCCNNWrapper + out_channels: ${model.feature_encoder.out_channels} + num_cell_dimensions: ${infer_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} + +readout: + _target_: topobench.nn.readouts.${model.readout.readout_name} + readout_name: PropagateSignalDown + num_cell_dimensions: ${infer_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} + hidden_dim: ${model.feature_encoder.out_channels} + out_channels: ${dataset.parameters.num_classes} + task_level: ${define_task_level:${dataset.parameters.task_level},${dataset.split_params.learning_setting}} + pooling_type: sum + +compile: false diff --git a/test/nn/backbones/simplicial/test_biscnn.py b/test/nn/backbones/simplicial/test_biscnn.py new file mode 100644 index 000000000..66cd2c0fd --- /dev/null +++ b/test/nn/backbones/simplicial/test_biscnn.py @@ -0,0 +1,195 @@ +"""Unit tests for the Bi-SCNN simplicial backbone.""" + +import pytest +import torch + +from topobench.nn.backbones.simplicial.biscnn import ( + BiSCNN, + BiSCNNLayer, + _hard_sign, + _row_l1_mean, +) + + +def _sparse_identity(size: int) -> torch.Tensor: + """Create a sparse identity matrix.""" + indices = torch.arange(size) + return torch.sparse_coo_tensor( + torch.stack((indices, indices)), + torch.ones(size), + (size, size), + ).coalesce() + + +@pytest.fixture +def simplicial_inputs(): + """Create node, edge, face features and compatible Laplacians.""" + x_all = ( + torch.randn(5, 3), + torch.randn(7, 4), + torch.randn(2, 5), + ) + laplacian_all = ( + _sparse_identity(5), + _sparse_identity(7), + _sparse_identity(7), + _sparse_identity(2), + _sparse_identity(2), + ) + incidence_all = ( + torch.sparse_coo_tensor(size=(5, 7)), + torch.sparse_coo_tensor(size=(7, 2)), + ) + return x_all, laplacian_all, incidence_all + + +def test_hard_sign_matches_paper_approximation(): + """Hard tanh must clip values while preserving the linear central region.""" + values = torch.tensor([-3.0, -0.5, 0.0, 0.5, 3.0]) + expected = torch.tensor([-1.0, -0.5, 0.0, 0.5, 1.0]) + torch.testing.assert_close(_hard_sign(values), expected) + + +def test_row_l1_mean(): + """Equation (23) is the row-wise L1 norm divided by feature count.""" + features = torch.tensor([[1.0, -3.0], [2.0, 4.0]]) + expected = torch.tensor([[2.0], [3.0]]) + torch.testing.assert_close(_row_l1_mean(features), expected) + + +def test_layer_keeps_weights_full_precision(): + """Bi-SCNN binarizes features, not trainable matrices.""" + layer = BiSCNNLayer(3, 4, use_lower=True, use_upper=True) + assert layer.lower_weight.dtype.is_floating_point + assert layer.upper_weight.dtype.is_floating_point + assert layer.harmonic_weight.dtype.is_floating_point + + +def test_layer_outputs_magnitude_and_binary(): + """Each paper layer must return its two propagation paths.""" + layer = BiSCNNLayer(3, 4, use_lower=True, use_upper=True) + features = torch.randn(6, 3) + operator = _sparse_identity(6) + magnitude, binary = layer(features, operator, operator) + assert magnitude.shape == (6, 1) + assert binary.shape == (6, 4) + assert torch.all(magnitude >= 0) + assert torch.all(binary <= 1) + assert torch.all(binary >= -1) + + +def test_forward_shapes_and_finiteness(simplicial_inputs): + """The backbone returns rank-specific TopoBench-compatible embeddings.""" + x_all, laplacian_all, incidence_all = simplicial_inputs + model = BiSCNN( + in_channels_all=(3, 4, 5), + hidden_channels_all=(8, 8, 8), + n_layers=2, + sc_order=3, + ) + outputs = model(x_all, laplacian_all, incidence_all) + assert [output.shape for output in outputs] == [(5, 8), (7, 8), (2, 8)] + assert all(torch.isfinite(output).all() for output in outputs) + + +def test_dense_and_sparse_operators_agree(): + """Dense and sparse Hodge multiplication must be numerically identical.""" + torch.manual_seed(7) + layer = BiSCNNLayer(3, 2, use_lower=True, use_upper=True) + features = torch.randn(4, 3) + dense = torch.eye(4) + sparse = dense.to_sparse() + dense_outputs = layer(features, dense, dense) + sparse_outputs = layer(features, sparse, sparse) + for dense_output, sparse_output in zip( + dense_outputs, sparse_outputs, strict=True + ): + torch.testing.assert_close(dense_output, sparse_output) + + +def test_sc_order_two_uses_lower_face_laplacian(): + """A two-dimensional complex has no rank-two upper Hodge component.""" + model = BiSCNN( + in_channels_all=(2, 2, 2), + hidden_channels_all=(3, 3, 3), + n_layers=1, + sc_order=2, + ) + x_all = (torch.randn(4, 2), torch.randn(5, 2), torch.randn(1, 2)) + laplacian_all = ( + _sparse_identity(4), + _sparse_identity(5), + _sparse_identity(5), + _sparse_identity(1), + ) + outputs = model(x_all, laplacian_all) + assert [output.shape for output in outputs] == [(4, 3), (5, 3), (1, 3)] + + +def test_gradients_flow_through_hard_tanh(simplicial_inputs): + """The hard-tanh proxy permits ordinary gradient-based optimization.""" + x_all, laplacian_all, incidence_all = simplicial_inputs + x_all = tuple(features.requires_grad_() for features in x_all) + model = BiSCNN( + in_channels_all=(3, 4, 5), + hidden_channels_all=(6, 6, 6), + n_layers=2, + sc_order=3, + ) + loss = sum( + output.square().mean() + for output in model(x_all, laplacian_all, incidence_all) + ) + loss.backward() + assert all(features.grad is not None for features in x_all) + assert all( + parameter.grad is not None + for parameter in model.parameters() + if parameter.requires_grad + ) + + +@pytest.mark.parametrize( + ("kwargs", "message"), + [ + ( + { + "in_channels_all": (3, 4), + "hidden_channels_all": (5, 5, 5), + }, + "node, edge, and face", + ), + ( + { + "in_channels_all": (3, 4, 5), + "hidden_channels_all": (5, 5, 5), + "n_layers": 0, + }, + "at least one", + ), + ( + { + "in_channels_all": (3, 4, 5), + "hidden_channels_all": (5, 5, 5), + "sc_order": 1, + }, + "at least two", + ), + ], +) +def test_invalid_model_arguments(kwargs, message): + """Invalid architecture arguments must fail clearly.""" + with pytest.raises(ValueError, match=message): + BiSCNN(**kwargs) + + +def test_invalid_laplacian_count(simplicial_inputs): + """The model rejects malformed TopoBench Laplacian tuples.""" + x_all, laplacian_all, _ = simplicial_inputs + model = BiSCNN( + in_channels_all=(3, 4, 5), + hidden_channels_all=(5, 5, 5), + sc_order=3, + ) + with pytest.raises(ValueError, match="expected 5 Laplacians"): + model(x_all, laplacian_all[:-1]) diff --git a/test/pipeline/test_pipeline.py b/test/pipeline/test_pipeline.py index a61165ae9..c62d6a441 100644 --- a/test/pipeline/test_pipeline.py +++ b/test/pipeline/test_pipeline.py @@ -7,7 +7,7 @@ DATASET = "graph/MUTAG" # ADD YOUR DATASET HERE -MODELS = ["graph/gcn", "cell/topotune", "simplicial/topotune"] # ADD ONE OR SEVERAL MODELS +MODELS = ["simplicial/biscnn"] # ADD ONE OR SEVERAL MODELS class TestPipeline: diff --git a/topobench/nn/backbones/simplicial/biscnn.py b/topobench/nn/backbones/simplicial/biscnn.py new file mode 100644 index 000000000..582ea8a13 --- /dev/null +++ b/topobench/nn/backbones/simplicial/biscnn.py @@ -0,0 +1,421 @@ +"""Binarized Simplicial Convolutional Neural Network. + +This module implements Bi-SCNN from: + + Yi Yan and Ercan E. Kuruoglu, + "Binarized Simplicial Convolutional Neural Networks", + Neural Networks 183 (2025), 106928. + arXiv:2405.04098. + +The implementation follows equations (21)--(27) of the paper. Features are +binarized with the hard-tanh approximation specified by the authors, while +the per-simplex magnitude is retained through row-wise L1 normalization. +Only features are binarized; trainable weights remain full precision. +""" + +from __future__ import annotations + +from collections.abc import Sequence + +import torch +from torch import nn + + +def _matmul( + operator: torch.Tensor | None, features: torch.Tensor +) -> torch.Tensor: + """Multiply a dense or sparse operator by a feature matrix. + + Parameters + ---------- + operator : torch.Tensor or None + Dense or sparse square simplicial operator. ``None`` represents a + missing lower or upper Hodge component. + features : torch.Tensor + Feature matrix of shape ``[num_simplices, num_features]``. + + Returns + ------- + torch.Tensor + Propagated feature matrix. + """ + if operator is None: + return torch.zeros_like(features) + if operator.layout == torch.strided: + return operator @ features + return torch.sparse.mm(operator, features) + + +def _hard_sign(features: torch.Tensor) -> torch.Tensor: + """Approximate the sign function with hard tanh. + + Parameters + ---------- + features : torch.Tensor + Input features. + + Returns + ------- + torch.Tensor + Features clipped to the interval ``[-1, 1]``. + + Notes + ----- + Section 4.5 of the paper states that ``Sign`` is approximated by hard + tanh so ordinary gradient-based optimization can be used. + """ + return torch.nn.functional.hardtanh(features, min_val=-1.0, max_val=1.0) + + +def _row_l1_mean(features: torch.Tensor) -> torch.Tensor: + """Compute the feature-normalization vector from equation (23). + + Parameters + ---------- + features : torch.Tensor + Feature matrix of shape ``[num_simplices, num_features]``. + + Returns + ------- + torch.Tensor + Nonnegative row-wise mean absolute value with shape + ``[num_simplices, 1]``. + """ + if features.shape[-1] == 0: + raise ValueError("features must contain at least one channel") + return features.abs().mean(dim=-1, keepdim=True) + + +class BiSCNNLayer(nn.Module): + """One weighted binary-sign simplicial convolution layer. + + For simplex rank ``k``, this layer computes the length-one simplicial + convolution from equation (24), then separates it into the magnitude and + binary-sign paths used by equations (25)--(27). + + Parameters + ---------- + in_channels : int + Number of input feature channels. + out_channels : int + Number of output feature channels. + use_lower : bool + Whether to include the lower-Hodge term. + use_upper : bool + Whether to include the upper-Hodge term. + bias : bool, optional + Whether to add a full-precision bias to the preactivation. + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + use_lower: bool, + use_upper: bool, + bias: bool = False, + ) -> None: + super().__init__() + if in_channels <= 0 or out_channels <= 0: + raise ValueError("channel dimensions must be positive") + if not use_lower and not use_upper: + raise ValueError("at least one Hodge component must be enabled") + + self.in_channels = in_channels + self.out_channels = out_channels + self.use_lower = use_lower + self.use_upper = use_upper + + self.lower_weight = ( + nn.Parameter(torch.empty(in_channels, out_channels)) + if use_lower + else None + ) + self.upper_weight = ( + nn.Parameter(torch.empty(in_channels, out_channels)) + if use_upper + else None + ) + self.harmonic_weight = nn.Parameter( + torch.empty(in_channels, out_channels) + ) + self.bias = nn.Parameter(torch.zeros(out_channels)) if bias else None + self.reset_parameters() + + def reset_parameters(self) -> None: + """Initialize all full-precision trainable matrices.""" + if self.lower_weight is not None: + nn.init.xavier_uniform_(self.lower_weight) + if self.upper_weight is not None: + nn.init.xavier_uniform_(self.upper_weight) + nn.init.xavier_uniform_(self.harmonic_weight) + if self.bias is not None: + nn.init.zeros_(self.bias) + + def forward( + self, + features: torch.Tensor, + lower_laplacian: torch.Tensor | None, + upper_laplacian: torch.Tensor | None, + ) -> tuple[torch.Tensor, torch.Tensor]: + """Compute the magnitude and binary-sign outputs of one layer. + + Parameters + ---------- + features : torch.Tensor + Input simplex features of shape + ``[num_simplices, in_channels]``. + lower_laplacian : torch.Tensor or None + Lower Hodge Laplacian for the current simplex rank. + upper_laplacian : torch.Tensor or None + Upper Hodge Laplacian for the current simplex rank. + + Returns + ------- + tuple of torch.Tensor + Row-wise normalization ``M`` and hard-sign output ``Q``. Their + shapes are ``[num_simplices, 1]`` and + ``[num_simplices, out_channels]``. + """ + if features.ndim != 2: + raise ValueError("features must be a rank-two tensor") + if features.shape[-1] != self.in_channels: + raise ValueError( + f"expected {self.in_channels} input channels, " + f"received {features.shape[-1]}" + ) + + preactivation = features @ self.harmonic_weight + if self.lower_weight is not None: + preactivation = ( + preactivation + + _matmul(lower_laplacian, features) @ self.lower_weight + ) + if self.upper_weight is not None: + preactivation = ( + preactivation + + _matmul(upper_laplacian, features) @ self.upper_weight + ) + if self.bias is not None: + preactivation = preactivation + self.bias + + magnitude = _row_l1_mean(preactivation) + binary = _hard_sign(preactivation) + return magnitude, binary + + +class BiSCNN(nn.Module): + """Binarized Simplicial Convolutional Neural Network backbone. + + A separate Bi-SCNN stack is applied to ranks 0, 1, and 2. In accordance + with the paper, intermediate layers propagate only the binary-sign output. + Per-layer normalization vectors bypass the binary path and are multiplied + into the final output. + + Parameters + ---------- + in_channels_all : sequence of int + Input dimensions for node, edge, and face features. + hidden_channels_all : sequence of int + Output dimensions for node, edge, and face features. + n_layers : int, optional + Number of Bi-SCNN layers. Must be at least one. + sc_order : int, optional + Maximum simplicial-complex order. Rank-2 upper propagation is enabled + when ``sc_order > 2``. + bias : bool, optional + Whether layers use full-precision biases. + **kwargs + Additional arguments accepted for TopoBench compatibility. + + Notes + ----- + The paper binarizes features but explicitly does not binarize trainable + weights. This implementation preserves that distinction. + """ + + def __init__( + self, + in_channels_all: Sequence[int], + hidden_channels_all: Sequence[int], + n_layers: int = 2, + sc_order: int = 3, + bias: bool = False, + **kwargs, + ) -> None: + super().__init__() + del kwargs + + if len(in_channels_all) != 3 or len(hidden_channels_all) != 3: + raise ValueError( + "in_channels_all and hidden_channels_all must contain " + "node, edge, and face dimensions" + ) + if n_layers < 1: + raise ValueError("n_layers must be at least one") + if sc_order < 2: + raise ValueError("sc_order must be at least two") + + self.in_channels_all = tuple(int(v) for v in in_channels_all) + self.hidden_channels_all = tuple(int(v) for v in hidden_channels_all) + self.out_channels = self.hidden_channels_all + self.n_layers = n_layers + self.sc_order = sc_order + + rank_flags = ( + (False, True), + (True, True), + (True, sc_order > 2), + ) + self.rank_layers = nn.ModuleList() + for rank, (use_lower, use_upper) in enumerate(rank_flags): + layers = nn.ModuleList() + for layer_index in range(n_layers): + input_dim = ( + self.in_channels_all[rank] + if layer_index == 0 + else self.hidden_channels_all[rank] + ) + layers.append( + BiSCNNLayer( + in_channels=input_dim, + out_channels=self.hidden_channels_all[rank], + use_lower=use_lower, + use_upper=use_upper, + bias=bias, + ) + ) + self.rank_layers.append(layers) + + @staticmethod + def _split_laplacians( + laplacian_all: Sequence[torch.Tensor], + sc_order: int, + ) -> tuple[ + tuple[torch.Tensor | None, torch.Tensor | None], + tuple[torch.Tensor | None, torch.Tensor | None], + tuple[torch.Tensor | None, torch.Tensor | None], + ]: + """Map TopoBench Laplacian tuples to lower/upper rank pairs. + + Parameters + ---------- + laplacian_all : sequence of torch.Tensor + For ``sc_order > 2`` this must contain ``L0, L1_down, L1_up, + L2_down, L2_up``. For ``sc_order == 2`` it must contain + ``L0, L1_down, L1_up, L2``. + sc_order : int + Simplicial-complex order. + + Returns + ------- + tuple + Lower/upper operator pairs for ranks 0, 1, and 2. + """ + expected = 5 if sc_order > 2 else 4 + if len(laplacian_all) != expected: + raise ValueError( + f"expected {expected} Laplacians for sc_order={sc_order}, " + f"received {len(laplacian_all)}" + ) + + if sc_order > 2: + lap_0, lap_1_down, lap_1_up, lap_2_down, lap_2_up = laplacian_all + else: + lap_0, lap_1_down, lap_1_up, lap_2_down = laplacian_all + lap_2_up = None + + return ( + (None, lap_0), + (lap_1_down, lap_1_up), + (lap_2_down, lap_2_up), + ) + + @staticmethod + def _run_rank( + features: torch.Tensor, + layers: nn.ModuleList, + lower_laplacian: torch.Tensor | None, + upper_laplacian: torch.Tensor | None, + ) -> torch.Tensor: + """Run one simplex rank through a Bi-SCNN stack. + + Parameters + ---------- + features : torch.Tensor + Input features for one simplex rank. + layers : torch.nn.ModuleList + Rank-specific Bi-SCNN layers. + lower_laplacian : torch.Tensor or None + Lower Hodge Laplacian. + upper_laplacian : torch.Tensor or None + Upper Hodge Laplacian. + + Returns + ------- + torch.Tensor + Final weighted binary-sign features. + """ + binary = features + accumulated_magnitude: torch.Tensor | None = None + for layer in layers: + magnitude, binary = layer( + binary, + lower_laplacian=lower_laplacian, + upper_laplacian=upper_laplacian, + ) + accumulated_magnitude = ( + magnitude + if accumulated_magnitude is None + else accumulated_magnitude * magnitude + ) + + if accumulated_magnitude is None: + raise RuntimeError("Bi-SCNN stack contains no layers") + return accumulated_magnitude * binary + + def forward( + self, + x_all: Sequence[torch.Tensor], + laplacian_all: Sequence[torch.Tensor], + incidence_all: Sequence[torch.Tensor] | None = None, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Compute node-, edge-, and face-level representations. + + Parameters + ---------- + x_all : sequence of torch.Tensor + Node, edge, and face feature matrices. + laplacian_all : sequence of torch.Tensor + TopoBench Hodge-Laplacian tuple. + incidence_all : sequence of torch.Tensor or None, optional + Incidence matrices accepted for wrapper compatibility. Bi-SCNN + uses the derived Hodge operators directly. + + Returns + ------- + tuple of torch.Tensor + Output feature matrices for ranks 0, 1, and 2. + """ + del incidence_all + if len(x_all) != 3: + raise ValueError( + "x_all must contain node, edge, and face features" + ) + + laplacian_pairs = self._split_laplacians( + laplacian_all, sc_order=self.sc_order + ) + outputs = [] + for features, layers, (lower, upper) in zip( + x_all, self.rank_layers, laplacian_pairs, strict=True + ): + outputs.append( + self._run_rank( + features, + layers, + lower_laplacian=lower, + upper_laplacian=upper, + ) + ) + return tuple(outputs)