diff --git a/Dockerfile b/Dockerfile index 3ba0297..88d07d2 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,4 +1,4 @@ -FROM nvidia/cuda:11.6.1-devel-ubuntu20.04 +FROM nvidia/cuda:12.1.1-devel-ubuntu20.04 # Add the current user to the image, to use the same user as the host. run the command "id" to find out your ids. ARG USER_NAME= @@ -37,7 +37,7 @@ RUN git config --global --add safe.directory /deps/mantra && git config --global RUN python3 -m venv /deps/venv && . /deps/venv/bin/activate && pip install --upgrade pip && pip install poetry RUN . /deps/venv/bin/activate && poetry install RUN . /deps/venv/bin/activate && pip install -e /deps/mantra/ /deps/TopoModelX/ -RUN . /deps/venv/bin/activate && pip install dgl -f https://data.dgl.ai/wheels/torch-2.3/cu118/repo.html +RUN . /deps/venv/bin/activate && pip install dgl -f https://data.dgl.ai/wheels/torch-2.3/cu121/repo.html # Set the default user to the new user USER $USER_NAME diff --git a/code/CellComplexCombinatorics.py b/code/CellComplexCombinatorics.py index d40ee2a..00c285b 100644 --- a/code/CellComplexCombinatorics.py +++ b/code/CellComplexCombinatorics.py @@ -4,15 +4,17 @@ def cc_incidence_matrix( - cell_complex: CellComplex, - rank: int, - signed: bool = True, - index: bool = False, + cell_complex: CellComplex, + rank: int, + signed: bool = True, + index: bool = False, ) -> scipy.sparse.csr_matrix | tuple[dict, dict, scipy.sparse.csr_matrix]: """ Same function as toponet but returns the boundary matrix of the cell complex in the correct order. """ - nodelist = cell_complex.nodes # order simplices as they appear in the cell complex + nodelist = ( + cell_complex.nodes + ) # order simplices as they appear in the cell complex if rank == 0: A = scipy.sparse.lil_matrix((0, len(nodelist))) if index: @@ -29,14 +31,15 @@ def cc_incidence_matrix( # edgelist contains edges composed by the indices of the vertices they contain sorted, # this is, with the induced orientation by the order of the vertices in the cell complex. edgelist = [ - sorted((node_index[e[0]], node_index[e[1]])) for e in cell_complex.edges + sorted((node_index[e[0]], node_index[e[1]])) + for e in cell_complex.edges ] if rank == 1: A = scipy.sparse.lil_matrix((len(nodelist), len(edgelist))) for ei, e in enumerate(edgelist): (ui, vi) = e[ - :2 - ] # Note that the indices are sorted, so we are orienting the edges + :2 + ] # Note that the indices are sorted, so we are orienting the edges # by the order of the nodes in the cell complex given by their indices A[ui, ei] = -1 A[vi, ei] = 1 @@ -79,7 +82,9 @@ def cc_incidence_matrix( # else: # A[ei, celli] = -1 if index: - cell_index = {c.elements: i for i, c in enumerate(cell_complex.cells)} + cell_index = { + c.elements: i for i, c in enumerate(cell_complex.cells) + } if signed: return edge_index, cell_index, A.asformat("csr") return edge_index, cell_index, abs(A.asformat("csr")) @@ -91,11 +96,13 @@ def cc_incidence_matrix( def hodge_laplacian_matrix( - cell_complex: CellComplex, - rank: int, - signed: bool = True, + cell_complex: CellComplex, + rank: int, + signed: bool = True, ) -> scipy.sparse.csr_matrix: - assert cell_complex.dim >= rank >= 0 # No negative dimensional Hodge Laplacian + assert ( + cell_complex.dim >= rank >= 0 + ) # No negative dimensional Hodge Laplacian if cell_complex.dim > rank >= 0: up_laplacian = up_laplacian_matrix(cell_complex, rank, True) else: @@ -124,9 +131,9 @@ def hodge_laplacian_matrix( def up_laplacian_matrix( - cell_complex: CellComplex, - rank: int, - signed: bool = True, + cell_complex: CellComplex, + rank: int, + signed: bool = True, ) -> scipy.sparse.csr_matrix: """ Same function as toponet but returns the upper laplacian of the cell complex in the correct order. @@ -145,7 +152,7 @@ def up_laplacian_matrix( def down_laplacian_matrix( - cell_complex: CellComplex, rank: int, signed: bool = True, weight=None + cell_complex: CellComplex, rank: int, signed: bool = True, weight=None ) -> scipy.sparse.csr_matrix: """ Same function as toponet but returns the lower laplacian of the cell complex in the correct order. @@ -166,7 +173,7 @@ def down_laplacian_matrix( def lower_adjacency( - cell_complex: CellComplex, dim: int, s: int = 1 + cell_complex: CellComplex, dim: int, s: int = 1 ) -> scipy.sparse.spmatrix: # A cell is neighbor of itself and all the other cells appearing in the lower hodge laplacian. if dim == 0: @@ -180,7 +187,7 @@ def lower_adjacency( def upper_adjacency( - cell_complex: CellComplex, dim: int, s: int = 1 + cell_complex: CellComplex, dim: int, s: int = 1 ) -> scipy.sparse.spmatrix: if cell_complex.dim == dim: match dim: @@ -198,6 +205,8 @@ def upper_adjacency( ) else: # A cell is neighbor of itself and all the other cells appearing in the upper hodge laplacian. - B_T = cc_incidence_matrix(cell_complex, dim + 1, signed=False).transpose() + B_T = cc_incidence_matrix( + cell_complex, dim + 1, signed=False + ).transpose() A = incidence_to_adjacency(B_T, s=s) return A.tocoo() diff --git a/code/datasets/transformer_dataloader.py b/code/datasets/transformer_dataloader.py index 0044b4b..b3aaf94 100644 --- a/code/datasets/transformer_dataloader.py +++ b/code/datasets/transformer_dataloader.py @@ -9,7 +9,11 @@ from datasets.utils import concat_tensors from datasets.utils import torch_sparse_to_scipy_sparse -from models.cells.transformer.DataTypes import CellComplexData, NeighborhoodMatrixType, NeighborhoodType +from models.cells.transformer.DataTypes import ( + CellComplexData, + NeighborhoodMatrixType, + NeighborhoodType, +) class TransformerDataloader(DataLoader): @@ -23,36 +27,67 @@ def sc_to_cell_complex_data(data): signals = data.x neighborhood_matrices = dict() for dim in signals.keys(): - if f'boundary_{dim}' in data.connectivity and dim > 0: - neighb_type = NeighborhoodMatrixType(NeighborhoodType.BOUNDARY, dim) - neighborhood_matrices[neighb_type] = torch_sparse_to_scipy_sparse(data.connectivity[f'boundary_{dim}']) - if f'adjacency_{dim}' in data.connectivity: - neighb_type = NeighborhoodMatrixType(NeighborhoodType.UPPER_ADJACENCY, dim) - neighborhood_matrices[neighb_type] = torch_sparse_to_scipy_sparse(data.connectivity[f'adjacency_{dim}']) - if f'coadjacency_{dim}' in data.connectivity: - neighb_type = NeighborhoodMatrixType(NeighborhoodType.LOWER_ADJACENCY, dim) - neighborhood_matrices[neighb_type] = torch_sparse_to_scipy_sparse(data.connectivity[f'coadjacency_{dim}']) - if f'up_laplacian_{dim}' in data.connectivity: - neighb_type = NeighborhoodMatrixType(NeighborhoodType.UPPER_HODGE_LAPLACIAN, dim) - neighborhood_matrices[neighb_type] = torch_sparse_to_scipy_sparse(data.connectivity[f'up_laplacian_{dim}']) - if f'down_laplacian_{dim}' in data.connectivity: - neighb_type = NeighborhoodMatrixType(NeighborhoodType.LOWER_HODGE_LAPLACIAN, dim) + if f"boundary_{dim}" in data.connectivity and dim > 0: + neighb_type = NeighborhoodMatrixType( + NeighborhoodType.BOUNDARY, dim + ) neighborhood_matrices[neighb_type] = torch_sparse_to_scipy_sparse( - data.connectivity[f'down_laplacian_{dim}']) - if f'hodge_{dim}' in data.connectivity: - neighb_type = NeighborhoodMatrixType(NeighborhoodType.HODGE_LAPLACIAN, dim) - neighborhood_matrices[neighb_type] = torch_sparse_to_scipy_sparse(data.connectivity[f'hodge_{dim}']) + data.connectivity[f"boundary_{dim}"] + ) + if f"adjacency_{dim}" in data.connectivity: + neighb_type = NeighborhoodMatrixType( + NeighborhoodType.UPPER_ADJACENCY, dim + ) + neighborhood_matrices[neighb_type] = torch_sparse_to_scipy_sparse( + data.connectivity[f"adjacency_{dim}"] + ) + if f"coadjacency_{dim}" in data.connectivity: + neighb_type = NeighborhoodMatrixType( + NeighborhoodType.LOWER_ADJACENCY, dim + ) + neighborhood_matrices[neighb_type] = torch_sparse_to_scipy_sparse( + data.connectivity[f"coadjacency_{dim}"] + ) + if f"up_laplacian_{dim}" in data.connectivity: + neighb_type = NeighborhoodMatrixType( + NeighborhoodType.UPPER_HODGE_LAPLACIAN, dim + ) + neighborhood_matrices[neighb_type] = torch_sparse_to_scipy_sparse( + data.connectivity[f"up_laplacian_{dim}"] + ) + if f"down_laplacian_{dim}" in data.connectivity: + neighb_type = NeighborhoodMatrixType( + NeighborhoodType.LOWER_HODGE_LAPLACIAN, dim + ) + neighborhood_matrices[neighb_type] = torch_sparse_to_scipy_sparse( + data.connectivity[f"down_laplacian_{dim}"] + ) + if f"hodge_{dim}" in data.connectivity: + neighb_type = NeighborhoodMatrixType( + NeighborhoodType.HODGE_LAPLACIAN, dim + ) + neighborhood_matrices[neighb_type] = torch_sparse_to_scipy_sparse( + data.connectivity[f"hodge_{dim}"] + ) other_features = dict() other_features["positional_encodings"] = data.pe - y = getattr(data, 'y', None) - return CellComplexData(signals=signals, neighborhood_matrices=neighborhood_matrices, other_features=other_features, - y=y) + y = getattr(data, "y", None) + return CellComplexData( + signals=signals, + neighborhood_matrices=neighborhood_matrices, + other_features=other_features, + y=y, + ) def collate_signals( - batch: Sequence[CellComplexData], -) -> tuple[dict[int, Float[torch.Tensor, "..."]], dict[int, Int[torch.Tensor, "..."]]]: - all_signals_keys = set([key for example in batch for key in example.signals]) + batch: Sequence[CellComplexData], +) -> tuple[ + dict[int, Float[torch.Tensor, "..."]], dict[int, Int[torch.Tensor, "..."]] +]: + all_signals_keys = set( + [key for example in batch for key in example.signals] + ) signals = dict() signals_belonging = dict() for key in all_signals_keys: @@ -70,24 +105,27 @@ def collate_signals( def collate_other_features_type_dependent( - feature_list: list[ - Float[torch.Tensor, "..."] - | dict[Any, Float[torch.Tensor, "..."]] - | list[Float[torch.Tensor, "..."]] - ] -) -> ( + feature_list: list[ Float[torch.Tensor, "..."] | dict[Any, Float[torch.Tensor, "..."]] | list[Float[torch.Tensor, "..."]] + ] +) -> ( + Float[torch.Tensor, "..."] + | dict[Any, Float[torch.Tensor, "..."]] + | list[Float[torch.Tensor, "..."]] ): data_example = feature_list[0] collated_subfeatures = None if isinstance(data_example, dict): collated_subfeatures = dict() - feature_names = set([key for example in feature_list for key in example.keys()]) + feature_names = set( + [key for example in feature_list for key in example.keys()] + ) for key in feature_names: collated_subfeatures[key] = torch.cat( - [example[key] for example in feature_list if key in example], dim=0 + [example[key] for example in feature_list if key in example], + dim=0, ) if isinstance(data_example, torch.Tensor): collated_subfeatures = concat_tensors(feature_list, dim=0) @@ -103,7 +141,7 @@ def collate_other_features_type_dependent( def collate_other_features( - batch: Sequence[CellComplexData], + batch: Sequence[CellComplexData], ) -> dict[Any, Float[torch.Tensor, "..."]]: feature_names = set( [ @@ -129,7 +167,7 @@ def collate_other_features( def collate_neighborhood_matrices( - batch: Sequence[CellComplexData], + batch: Sequence[CellComplexData], ) -> dict[NeighborhoodMatrixType, scipy.sparse.spmatrix | SparseMatrix]: all_neighborhood_matrices_keys = set( [ @@ -141,26 +179,28 @@ def collate_neighborhood_matrices( ) neighborhood_matrices = dict() for key in all_neighborhood_matrices_keys: - neighborhood_matrices[key] = convert_sparse_matrices_to_sparse_block_matrix( - key, - [ - # Get the neighborhood matrix if it exists, otherwise None - ( - example.neighborhood_matrices[key] - if key in example.neighborhood_matrices - else None - ) - for example in batch - ], - batch, + neighborhood_matrices[key] = ( + convert_sparse_matrices_to_sparse_block_matrix( + key, + [ + # Get the neighborhood matrix if it exists, otherwise None + ( + example.neighborhood_matrices[key] + if key in example.neighborhood_matrices + else None + ) + for example in batch + ], + batch, + ) ) return neighborhood_matrices def convert_sparse_matrices_to_sparse_block_matrix( - key: NeighborhoodMatrixType, - sparse_matrices: list[Optional[scipy.sparse.spmatrix | SparseMatrix]], - batch: list[CellComplexData], + key: NeighborhoodMatrixType, + sparse_matrices: list[Optional[scipy.sparse.spmatrix | SparseMatrix]], + batch: list[CellComplexData], ) -> scipy.sparse.spmatrix: rows, columns, values = [], [], [] idx_rows, idx_cols = 0, 0 @@ -178,11 +218,11 @@ def convert_sparse_matrices_to_sparse_block_matrix( # Depending on the neighborhood matrix, we need to perform different operations match key.type: case ( - NeighborhoodType.UPPER_ADJACENCY - | NeighborhoodType.LOWER_ADJACENCY - | NeighborhoodType.LOWER_HODGE_LAPLACIAN - | NeighborhoodType.UPPER_HODGE_LAPLACIAN - | NeighborhoodType.HODGE_LAPLACIAN + NeighborhoodType.UPPER_ADJACENCY + | NeighborhoodType.LOWER_ADJACENCY + | NeighborhoodType.LOWER_HODGE_LAPLACIAN + | NeighborhoodType.UPPER_HODGE_LAPLACIAN + | NeighborhoodType.HODGE_LAPLACIAN ): # In this case we have two possibilities. # (1) There are no cells of that dimension in the cell complex, so we do not need to add any @@ -192,7 +232,9 @@ def convert_sparse_matrices_to_sparse_block_matrix( # hodge laplacian matrix). In this case, we should add as many rows and columns as there are cells # of that dimension in the cell complex. if c_complex.dim >= key.dimension: - c_dim_cardinality = len(c_complex.signals[key.dimension]) + c_dim_cardinality = len( + c_complex.signals[key.dimension] + ) idx_rows += c_dim_cardinality idx_cols += c_dim_cardinality case NeighborhoodType.BOUNDARY: @@ -203,12 +245,16 @@ def convert_sparse_matrices_to_sparse_block_matrix( # add no rows or columns. if c_complex.dim == key.dimension - 1: - c_dim_cardinality = len(c_complex.signals[key.dimension - 1]) + c_dim_cardinality = len( + c_complex.signals[key.dimension - 1] + ) idx_rows += c_dim_cardinality else: pass case _: - raise ValueError(f"Unknown neighborhood matrix type {key.type}") + raise ValueError( + f"Unknown neighborhood matrix type {key.type}" + ) else: coo_matrix = matrix.tocoo() len_rows, len_cols = coo_matrix.shape @@ -217,7 +263,10 @@ def convert_sparse_matrices_to_sparse_block_matrix( coo_matrix.col, coo_matrix.data, ) - rows_abs, cols_abs = rows_example + idx_rows, cols_example + idx_cols + rows_abs, cols_abs = ( + rows_example + idx_rows, + cols_example + idx_cols, + ) rows.append(rows_abs) columns.append(cols_abs) values.append(values_example) @@ -227,11 +276,11 @@ def convert_sparse_matrices_to_sparse_block_matrix( columns_cat = np.concatenate(columns, axis=0) values_cat = np.concatenate(values, axis=0) if idx_rows != sum( - [ - example.signals[key.dimension - 1].shape[0] - for example in batch - if (key.dimension - 1) in example.signals - ] + [ + example.signals[key.dimension - 1].shape[0] + for example in batch + if (key.dimension - 1) in example.signals + ] ) and idx_rows != sum( [ example.signals[key.dimension].shape[0] @@ -256,13 +305,17 @@ def collate_cell_complex(batch: list[CellComplexData]) -> CellComplexData: other_features["signals_belonging"] = signals_belonging # Collate labels # We only concatenate y's for not None labels. - y = concat_tensors([data.y for data in batch], dim=0) if batch[0].y is not None else None + y = ( + concat_tensors([data.y for data in batch], dim=0) + if batch[0].y is not None + else None + ) return CellComplexData( signals=signals, neighborhood_matrices=neighborhood_matrices, other_features=other_features, batch_size=len(batch), - y=y + y=y, ) diff --git a/code/datasets/utils.py b/code/datasets/utils.py index f7b4b6d..1285709 100644 --- a/code/datasets/utils.py +++ b/code/datasets/utils.py @@ -90,29 +90,29 @@ def format_matrix(matrix): if connectivity_info == "incidence" and rank_idx == 0: continue try: - connectivity[ - f"{connectivity_info}_{rank_idx}" - ] = format_matrix( - getattr(complex, f"{connectivity_info}_matrix")( - rank=rank_idx, signed=False + connectivity[f"{connectivity_info}_{rank_idx}"] = ( + format_matrix( + getattr(complex, f"{connectivity_info}_matrix")( + rank=rank_idx, signed=False + ) ) ) except ValueError: if connectivity_info == "incidence": - connectivity[ - f"{connectivity_info}_{rank_idx}" - ] = generate_zero_sparse_connectivity( - m=practical_shape[rank_idx - 1], - n=practical_shape[rank_idx], - scipy_format=keep_scipy, + connectivity[f"{connectivity_info}_{rank_idx}"] = ( + generate_zero_sparse_connectivity( + m=practical_shape[rank_idx - 1], + n=practical_shape[rank_idx], + scipy_format=keep_scipy, + ) ) else: - connectivity[ - f"{connectivity_info}_{rank_idx}" - ] = generate_zero_sparse_connectivity( - m=practical_shape[rank_idx], - n=practical_shape[rank_idx], - scipy_format=keep_scipy, + connectivity[f"{connectivity_info}_{rank_idx}"] = ( + generate_zero_sparse_connectivity( + m=practical_shape[rank_idx], + n=practical_shape[rank_idx], + scipy_format=keep_scipy, + ) ) if rank_idx == 0: continue @@ -123,12 +123,12 @@ def format_matrix(matrix): ) ) except ValueError: - connectivity[ - f"{connectivity_info}_{rank_idx}" - ] = generate_zero_sparse_connectivity( - m=practical_shape[rank_idx - 1], - n=practical_shape[rank_idx], - scipy_format=keep_scipy, + connectivity[f"{connectivity_info}_{rank_idx}"] = ( + generate_zero_sparse_connectivity( + m=practical_shape[rank_idx - 1], + n=practical_shape[rank_idx], + scipy_format=keep_scipy, + ) ) """ diff --git a/code/experiments/utils/result_collection.py b/code/experiments/utils/result_collection.py index b1da4bd..22d79a9 100644 --- a/code/experiments/utils/result_collection.py +++ b/code/experiments/utils/result_collection.py @@ -60,9 +60,9 @@ def save(self, t_file_prefix: str = "res"): result["type_model"] = x.config.conf_model.type.name.lower() result["transform"] = x.config.transforms.name.lower() result["ds_type"] = x.config.ds_type.name.lower() - result[ - "barycentric_subdivision_idx" - ] = x.barycentric_subdivision_idx + result["barycentric_subdivision_idx"] = ( + x.barycentric_subdivision_idx + ) data.append(result) df = pd.DataFrame(data) diff --git a/code/experiments/vis/us_cmap.py b/code/experiments/vis/us_cmap.py index c676352..41b5294 100644 --- a/code/experiments/vis/us_cmap.py +++ b/code/experiments/vis/us_cmap.py @@ -38,9 +38,9 @@ def scale_white_amount(rgb, percent): def register_name(new_name, color): try: # just a relabeling of the name - matplotlib.colors.ColorConverter.colors[ - new_name - ] = matplotlib.colors.ColorConverter.colors[color] + matplotlib.colors.ColorConverter.colors[new_name] = ( + matplotlib.colors.ColorConverter.colors[color] + ) except KeyError: if type(color) is str: color = get_rgba(color) @@ -65,9 +65,9 @@ def activate(): list_cmap.append(rgba) matplotlib.colors.ColorConverter.colors[name] = rgba for step in steps: - matplotlib.colors.ColorConverter.colors[ - f"{name}!{step * 100:.0f}" - ] = scale_white_amount(values, step) + matplotlib.colors.ColorConverter.colors[f"{name}!{step * 100:.0f}"] = ( + scale_white_amount(values, step) + ) matplotlib.cm.register_cmap( name="US", cmap=matplotlib.colors.ListedColormap(list_cmap) diff --git a/code/models/cells/transformer/CellularTransformer.py b/code/models/cells/transformer/CellularTransformer.py index d2fc49d..3a9ba6d 100644 --- a/code/models/cells/transformer/CellularTransformer.py +++ b/code/models/cells/transformer/CellularTransformer.py @@ -10,7 +10,11 @@ from math_utils import sparse_abs from ...model_types import ModelType -from models.cells.transformer.DataTypes import NeighborhoodMatrixType, NeighborhoodType, CellComplexData +from models.cells.transformer.DataTypes import ( + NeighborhoodMatrixType, + NeighborhoodType, + CellComplexData, +) from models.cells.transformer.InputPreprocessing import ( InputPreprocessing, generate_input_preprocessing_layer, @@ -26,12 +30,15 @@ from models.cells.transformer.layers.attention.PointCloudAttentionLayer import ( PointCloudAttentionLayer, ) -from models.cells.transformer.positional_encodings.PositionalEncodings import PositionalEncodingsType +from models.cells.transformer.positional_encodings.PositionalEncodings import ( + PositionalEncodingsType, +) -def _get_batch_mask_improved(s: Float[torch.Tensor, "1 source_simplices"], - t: Float[torch.Tensor, "1 target_simplices"]) \ - -> Float[torch.Tensor, "target_simplices source_simplices"]: +def _get_batch_mask_improved( + s: Float[torch.Tensor, "1 source_simplices"], + t: Float[torch.Tensor, "1 target_simplices"], +) -> Float[torch.Tensor, "target_simplices source_simplices"]: mask_n = torch.zeros((len(t), len(s)), dtype=torch.float32) ps_start, pt_start = 0, 0 ps, pt = 0, 0 @@ -66,13 +73,23 @@ def _get_batch_mask_improved(s: Float[torch.Tensor, "1 source_simplices"], return mask_n -def _get_batch_mask(x: CellComplexData, interaction: Interaction, improved: bool = False): - target_belongings = x.other_features["signals_belonging"][interaction.out_dim] - source_belongings = x.other_features["signals_belonging"][interaction.in_dim] +def _get_batch_mask( + x: CellComplexData, interaction: Interaction, improved: bool = False +): + target_belongings = x.other_features["signals_belonging"][ + interaction.out_dim + ] + source_belongings = x.other_features["signals_belonging"][ + interaction.in_dim + ] if improved: - batch_mask_dense = _get_batch_mask_improved(s=source_belongings, t=target_belongings).to_sparse() + batch_mask_dense = _get_batch_mask_improved( + s=source_belongings, t=target_belongings + ).to_sparse() else: - batch_mask_dense = (target_belongings.unsqueeze(1) == source_belongings).to_sparse() + batch_mask_dense = ( + target_belongings.unsqueeze(1) == source_belongings + ).to_sparse() batch_mask = dglsp.spmatrix( indices=batch_mask_dense.indices().to(target_belongings.device), shape=tuple(batch_mask_dense.shape), @@ -94,7 +111,8 @@ def _get_attention_mask(x: CellComplexData, interaction: Interaction): attention_masks = x.neighborhood_matrices[neighborhood_type] else: neighborhood_type = NeighborhoodMatrixType( - type=NeighborhoodType.LOWER_ADJACENCY, dimension=interaction.in_dim + type=NeighborhoodType.LOWER_ADJACENCY, + dimension=interaction.in_dim, ) attention_masks = x.neighborhood_matrices[neighborhood_type] elif interaction.in_dim == interaction.out_dim - 1: @@ -108,7 +126,9 @@ def _get_attention_mask(x: CellComplexData, interaction: Interaction): neighborhood_type = NeighborhoodMatrixType( type=NeighborhoodType.BOUNDARY, dimension=interaction.in_dim ) - attention_masks = sparse_abs(x.neighborhood_matrices[neighborhood_type]) + attention_masks = sparse_abs( + x.neighborhood_matrices[neighborhood_type] + ) else: attention_masks = dglsp.spmatrix( indices=torch.tensor([[0], [0]]).to(x.signals[0].device), @@ -122,11 +142,13 @@ def _get_attention_mask(x: CellComplexData, interaction: Interaction): def _get_layer_mask_types( - attention_mask_types: Optional[list[MaskType, ...] | MaskType], - number_of_layers: int, + attention_mask_types: Optional[list[MaskType, ...] | MaskType], + number_of_layers: int, ) -> list[MaskType, ...]: if attention_mask_types is None: - attention_mask_types = [MaskType.NO_MASK for _ in range(number_of_layers)] + attention_mask_types = [ + MaskType.NO_MASK for _ in range(number_of_layers) + ] else: if not isinstance(attention_mask_types, list): # If only one mask type is provided, we repeat it for all layers. @@ -134,15 +156,16 @@ def _get_layer_mask_types( attention_mask_types for _ in range(number_of_layers) ] assert len(attention_mask_types) == number_of_layers, ( - "The number of attention mask types must be equal " "to the number of layers." + "The number of attention mask types must be equal " + "to the number of layers." ) return attention_mask_types def _get_layer_tensor_diagrams( - tensor_diagram_input: Optional[list[TensorDiagram, ...] | TensorDiagram], - input_sizes: dict[int, int], - number_of_layers: int, + tensor_diagram_input: Optional[list[TensorDiagram, ...] | TensorDiagram], + input_sizes: dict[int, int], + number_of_layers: int, ) -> list[TensorDiagram, ...]: if tensor_diagram_input is None: # In case tensor diagram(s) is (are) not provided, we use a fully connected tensor diagram. @@ -157,7 +180,9 @@ def _get_layer_tensor_diagrams( ] ) tensor_diagram = TensorDiagram(tensor_diag_str) - tensor_diagram_input = [tensor_diagram for _ in range(number_of_layers)] + tensor_diagram_input = [ + tensor_diagram for _ in range(number_of_layers) + ] else: if not isinstance(tensor_diagram_input, list): # If only one tensor diagram is provided, we repeat it for all layers. @@ -166,25 +191,32 @@ def _get_layer_tensor_diagrams( ] assert len(tensor_diagram_input) == number_of_layers, ( - "The number of tensor diagrams must be equal " "to the number of layers." + "The number of tensor diagrams must be equal " + "to the number of layers." ) return tensor_diagram_input def _get_input_preprocessing_layers( - input_preprocessing_type: dict[int, InputPreprocessing] | InputPreprocessing, - input_sizes: dict[int, int], - positional_encodings_lengths: dict[int, int], - hidden_size: int, - initialization: WeightInitialization, + input_preprocessing_type: ( + dict[int, InputPreprocessing] | InputPreprocessing + ), + input_sizes: dict[int, int], + positional_encodings_lengths: dict[int, int], + hidden_size: int, + initialization: WeightInitialization, ) -> nn.ModuleList: if isinstance(input_preprocessing_type, dict): - assert set(input_preprocessing_type.keys()) == set(input_sizes.keys()), ( + assert set(input_preprocessing_type.keys()) == set( + input_sizes.keys() + ), ( "The keys of the input preprocessing type dictionary must be the same as the keys of the input sizes" " dictionary." ) else: - input_preprocessing_type = [input_preprocessing_type for _ in input_sizes] + input_preprocessing_type = [ + input_preprocessing_type for _ in input_sizes + ] input_preprocessing_layers = nn.ModuleList( [ generate_input_preprocessing_layer( @@ -215,23 +247,26 @@ class CellularTransformerConfig(BaseModel): drop_path_probability: float = 0.0 num_hidden_layers_last_mlp: int = 2 use_bias: bool = True - forget_dimensions: bool = False # If True, layer_tensor_diagrams must be None and attention + forget_dimensions: bool = ( + False # If True, layer_tensor_diagrams must be None and attention + ) # is performed with cells as points in a point cloud, forgetting the dimensions. class CellularTransformer(nn.Module): - def __init__( - self, - config: CellularTransformerConfig - ): + def __init__(self, config: CellularTransformerConfig): super().__init__() self.input_sizes = config.input_sizes self.readout = Readout.GLOBAL_MEAN_POOLING # Fixed self.num_layers = config.num_layers self.num_heads = config.num_heads - self.positional_encoding_type = PositionalEncodingsType.HODGE_LAPLACIAN_EIGENVECTORS # Fixed - self.input_preprocessing_type = InputPreprocessing.SUM_POSITIONAL_ENCODINGS # Fixed + self.positional_encoding_type = ( + PositionalEncodingsType.HODGE_LAPLACIAN_EIGENVECTORS + ) # Fixed + self.input_preprocessing_type = ( + InputPreprocessing.SUM_POSITIONAL_ENCODINGS + ) # Fixed self.dropout_attention = config.dropout_attention self.dropout_activations = config.dropout_activations self.drop_path_probability = config.drop_path_probability @@ -241,7 +276,9 @@ def __init__( self.initialization = WeightInitialization.XAVIER_UNIFORM # Fixed. self.dropout_final_mlp = config.dropout_final_mlp self.out_size = config.out_size - self.layer_tensor_diagrams = TensorDiagram("0->0,0->1,1->0,1->1,1->2,2->1,2->2") # Fixed + self.layer_tensor_diagrams = TensorDiagram( + "0->0,0->1,1->0,1->1,1->2,2->1,2->2" + ) # Fixed attention_mask_types = MaskType.SUM # Fixed # Configuring the attention type. Here, we decide if we drop the hierarchical structure of the # cellular_data complex to perform attention. @@ -251,9 +288,7 @@ def __init__( "If forget_dimensions is True, layer_tensor_diagrams must be None." ) if self.forget_dimensions: - layer_tensor_diagrams = ( - None # We use a fully connected tensor diagram, that is generated when - ) + layer_tensor_diagrams = None # We use a fully connected tensor diagram, that is generated when # layer tensor diagrams is None. self.layer_tensor_diagrams = _get_layer_tensor_diagrams( self.layer_tensor_diagrams, config.input_sizes, config.num_layers @@ -276,19 +311,28 @@ def __init__( PositionalEncodingsType.BARYCENTRIC_SUBDIVISION_GRAPH_EIGENVECTORS, ] self.input_dropout_layers = nn.ModuleList( - [nn.Dropout(config.dropout_input_projections) for _ in config.input_sizes.keys()] + [ + nn.Dropout(config.dropout_input_projections) + for _ in config.input_sizes.keys() + ] ) # Attention layers self.attention_layers = self.get_attention_layers() if self.readout == Readout.SET2SET_POOLING: - self.readout_layer = get_readout_layer(self.readout, input_dim=config.hidden_size) + self.readout_layer = get_readout_layer( + self.readout, input_dim=config.hidden_size + ) else: self.readout_layer = get_readout_layer(self.readout) # Final MLP block and readout layer self.predictor_head = self.get_prediction_head() def get_prediction_head(self) -> Optional[nn.ModuleDict | nn.Module]: - in_features = self.hidden_size if self.readout != Readout.SET2SET_POOLING else self.readout_layer.output_dim + in_features = ( + self.hidden_size + if self.readout != Readout.SET2SET_POOLING + else self.readout_layer.output_dim + ) if self.readout == Readout.ALL_GLOBAL_ADD_POOLING: return nn.ModuleDict( { @@ -332,7 +376,9 @@ def get_attention_layers(self) -> nn.ModuleList: activation_dropout=self.dropout_activations, drop_path_probability=self.drop_path_probability, initialization=self.initialization, - attention_mask_type=self.attention_mask_types[layer_idx], + attention_mask_type=self.attention_mask_types[ + layer_idx + ], ) for layer_idx in range(self.num_layers) ] @@ -349,14 +395,16 @@ def get_attention_layers(self) -> nn.ModuleList: drop_path_probability=self.drop_path_probability, initialization=self.initialization, tensor_diagram=self.layer_tensor_diagrams[layer_idx], - attention_mask_type=self.attention_mask_types[layer_idx], + attention_mask_type=self.attention_mask_types[ + layer_idx + ], ) for layer_idx in range(self.num_layers) ] ) def apply_random_sign_flip_if_needed( - self, positional_encodings: dict[int, Float[torch.Tensor, "..."]] + self, positional_encodings: dict[int, Float[torch.Tensor, "..."]] ) -> dict[int, Float[torch.Tensor, "..."]]: with torch.no_grad(): if self.training and self.flip_sign_pe: @@ -366,11 +414,14 @@ def apply_random_sign_flip_if_needed( for dim in positional_encodings: pe_dim = positional_encodings[dim] rand_sign = ( - 2 - * ( - torch.rand(pe_dim.shape[0], 1, device=pe_dim.device) > 0.5 - ).float() - - 1.0 + 2 + * ( + torch.rand( + pe_dim.shape[0], 1, device=pe_dim.device + ) + > 0.5 + ).float() + - 1.0 ) pe_dim_updated = rand_sign * pe_dim updated_positional_encodings[dim] = pe_dim_updated @@ -379,7 +430,7 @@ def apply_random_sign_flip_if_needed( return positional_encodings def get_attention_matrices( - self, x: CellComplexData + self, x: CellComplexData ) -> dict[Interaction, SparseMatrix]: attention_masks = dict() # We iterate over all possible interactions through the whole set of layers. @@ -387,10 +438,14 @@ def get_attention_matrices( for interaction in tensor_diagram.interactions: # We avoid recomputing the attention mask if it has already been computed. if interaction not in attention_masks: - attention_masks[interaction] = _get_attention_mask(x, interaction) + attention_masks[interaction] = _get_attention_mask( + x, interaction + ) return attention_masks - def get_batch_matrices(self, x: CellComplexData) -> dict[Interaction, SparseMatrix]: + def get_batch_matrices( + self, x: CellComplexData + ) -> dict[Interaction, SparseMatrix]: batch_masks = dict() # We iterate over all possible interactions through the whole set of layers. for tensor_diagram in self.layer_tensor_diagrams: @@ -401,11 +456,11 @@ def get_batch_matrices(self, x: CellComplexData) -> dict[Interaction, SparseMatr return batch_masks def forward_readout( - self, - h: dict[int, Float[torch.Tensor, "..."]], - h_belongings: dict[int, Int[torch.Tensor, "total_signals"]], + self, + h: dict[int, Float[torch.Tensor, "..."]], + h_belongings: dict[int, Int[torch.Tensor, "total_signals"]], ) -> Float[torch.Tensor, "..."]: - # Global readouts are computed using all possible features at the final attention layer. + # Global readouts are computed using all possible features at the final attention layer. # No readout is a layer that do not perform any computation and that directly accepts h and # h_belongings as input. if self.readout == Readout.NO_READOUT: @@ -429,36 +484,48 @@ def forward_readout( h = self.predictor_head(h[0]) match self.readout: case Readout.GLOBAL_ADD_POOLING: - out = self.readout_layer(x={0: h}, x_belongings={0: h_belongings[0]}) + out = self.readout_layer( + x={0: h}, x_belongings={0: h_belongings[0]} + ) out = ( - out[0] / self.num_heads + out[0] / self.num_heads ) # We use the vertex outputs and we normalize by the number of heads. case Readout.GLOBAL_MEAN_POOLING: - out = self.readout_layer(x={0: h}, x_belongings={0: h_belongings[0]}) + out = self.readout_layer( + x={0: h}, x_belongings={0: h_belongings[0]} + ) out = out[0] # We do not normalize in this case as the mean pooling already does it. case Readout.GLOBAL_MAX_POOLING: - out = self.readout_layer(x={0: h}, x_belongings={0: h_belongings[0]}) + out = self.readout_layer( + x={0: h}, x_belongings={0: h_belongings[0]} + ) out = out[0] # We do not normalize in this case as the max pooling only selects the value of one vertex. case Readout.GLOBAL_BASIC_COMBINATION_POOLING: - out = self.readout_layer(x={0: h}, x_belongings={0: h_belongings[0]}) + out = self.readout_layer( + x={0: h}, x_belongings={0: h_belongings[0]} + ) out = out[0] # We do not normalize in this case as we are learning a weight for the sum and combining with the other # two simple readouts mean and max. case _: - raise NotImplementedError(f"Readout {self.readout} is not implemented.") + raise NotImplementedError( + f"Readout {self.readout} is not implemented." + ) return out def forward_point_cloud_attention( - self, - h: dict[int, Float[torch.Tensor, "..."]], - batch_matrices: dict[Interaction, SparseMatrix], - attention_matrices: dict[Interaction, SparseMatrix], + self, + h: dict[int, Float[torch.Tensor, "..."]], + batch_matrices: dict[Interaction, SparseMatrix], + attention_matrices: dict[Interaction, SparseMatrix], ) -> dict[int, Float[torch.Tensor, "..."]]: (h_joint, joint_batch_matrix, joint_attention_matrix) = ( PointCloudAttentionLayer.get_joint_signals_and_batch_and_attention_matrices( - x=h, batch_masks=batch_matrices, attention_masks=attention_matrices + x=h, + batch_masks=batch_matrices, + attention_masks=attention_matrices, ) ) for layer_idx in range(self.num_layers): @@ -471,20 +538,25 @@ def forward_point_cloud_attention( return h_updated def forward_hierarchical_attention( - self, - h: dict[int, Float[torch.Tensor, "..."]], - batch_matrices: dict[Interaction, SparseMatrix], - attention_matrices: dict[Interaction, SparseMatrix], + self, + h: dict[int, Float[torch.Tensor, "..."]], + batch_matrices: dict[Interaction, SparseMatrix], + attention_matrices: dict[Interaction, SparseMatrix], ) -> dict[int, Float[torch.Tensor, "..."]]: for layer_idx in range(self.num_layers): - h = self.attention_layers[layer_idx](h, batch_matrices, attention_matrices) + h = self.attention_layers[layer_idx]( + h, batch_matrices, attention_matrices + ) return h def forward(self, x: CellComplexData) -> Float[torch.Tensor, "..."]: # h will contain the updated signals at each step. h = dict() # First we get the positional encodings and apply a random flip of the sign if needed. - if self.input_preprocessing_type == InputPreprocessing.NO_POSITIONAL_ENCODINGS: + if ( + self.input_preprocessing_type + == InputPreprocessing.NO_POSITIONAL_ENCODINGS + ): positional_encodings = None else: positional_encodings = x.other_features["positional_encodings"] @@ -499,7 +571,8 @@ def forward(self, x: CellComplexData) -> Float[torch.Tensor, "..."]: for dim in x.signals: pe_dim = ( None - if self.input_preprocessing_type == InputPreprocessing.NO_POSITIONAL_ENCODINGS + if self.input_preprocessing_type + == InputPreprocessing.NO_POSITIONAL_ENCODINGS else positional_encodings[dim] ) h[dim] = self.preproc_layers[dim](x.signals[dim], pe_dim) diff --git a/code/models/cells/transformer/InputPreprocessing.py b/code/models/cells/transformer/InputPreprocessing.py index c81e39a..7cb8d15 100644 --- a/code/models/cells/transformer/InputPreprocessing.py +++ b/code/models/cells/transformer/InputPreprocessing.py @@ -18,7 +18,9 @@ class InputPreprocessing(Enum): CONCATENATION_POSITIONAL_ENCODINGS = "concatenation_positional_encodings" -def get_input_preprocessing_layer(input_preprocessing_type: InputPreprocessing): +def get_input_preprocessing_layer( + input_preprocessing_type: InputPreprocessing, +): match input_preprocessing_type: case InputPreprocessing.SUM_POSITIONAL_ENCODINGS: return SumPositionalEncoding @@ -33,11 +35,11 @@ def get_input_preprocessing_layer(input_preprocessing_type: InputPreprocessing): def generate_input_preprocessing_layer( - input_preprocessing_type: InputPreprocessing, - dim_features: int, - dim_positional_encoding: int, - hidden_dim: int, - initialization: WeightInitialization, + input_preprocessing_type: InputPreprocessing, + dim_features: int, + dim_positional_encoding: int, + hidden_dim: int, + initialization: WeightInitialization, ): input_preproccesing_layer_class = get_input_preprocessing_layer( input_preprocessing_type diff --git a/code/models/cells/transformer/Readout.py b/code/models/cells/transformer/Readout.py index 7dc3fea..15a72ad 100644 --- a/code/models/cells/transformer/Readout.py +++ b/code/models/cells/transformer/Readout.py @@ -1,14 +1,24 @@ from enum import Enum -from models.cells.transformer.layers.readouts.AllGlobalAddPooling import AllGlobalAddPooling +from models.cells.transformer.layers.readouts.AllGlobalAddPooling import ( + AllGlobalAddPooling, +) from models.cells.transformer.layers.readouts.GlobalAddPooling import ( GlobalAddPooling, ) -from models.cells.transformer.layers.readouts.GlobalBasicCombinationPooling import GlobalBasicCombinationPooling -from models.cells.transformer.layers.readouts.GlobalMaxPooling import GlobalMaxPooling -from models.cells.transformer.layers.readouts.GlobalMeanPooling import GlobalMeanPooling +from models.cells.transformer.layers.readouts.GlobalBasicCombinationPooling import ( + GlobalBasicCombinationPooling, +) +from models.cells.transformer.layers.readouts.GlobalMaxPooling import ( + GlobalMaxPooling, +) +from models.cells.transformer.layers.readouts.GlobalMeanPooling import ( + GlobalMeanPooling, +) from models.cells.transformer.layers.readouts.NoReadout import NoReadout -from models.cells.transformer.layers.readouts.Set2SetPooling import Set2SetPooling +from models.cells.transformer.layers.readouts.Set2SetPooling import ( + Set2SetPooling, +) class Readout(Enum): @@ -41,4 +51,6 @@ def get_readout_layer(readout_type: Readout, **kwargs): case Readout.GLOBAL_BASIC_COMBINATION_POOLING: return GlobalBasicCombinationPooling() case _: - raise ValueError(f"Readout layer type {readout_type} not recognized.") + raise ValueError( + f"Readout layer type {readout_type} not recognized." + ) diff --git a/code/models/cells/transformer/WeightInitialization.py b/code/models/cells/transformer/WeightInitialization.py index 77b512a..5be608c 100644 --- a/code/models/cells/transformer/WeightInitialization.py +++ b/code/models/cells/transformer/WeightInitialization.py @@ -10,7 +10,7 @@ class WeightInitialization(Enum): def get_initialization_function( - initialization: WeightInitialization, gain: float = 1.0 + initialization: WeightInitialization, gain: float = 1.0 ): match initialization: case WeightInitialization.XAVIER_UNIFORM: diff --git a/code/models/cells/transformer/layers/attention/SparseMultiHeadAttention.py b/code/models/cells/transformer/layers/attention/SparseMultiHeadAttention.py index eb9ce71..f190067 100644 --- a/code/models/cells/transformer/layers/attention/SparseMultiHeadAttention.py +++ b/code/models/cells/transformer/layers/attention/SparseMultiHeadAttention.py @@ -46,7 +46,7 @@ def __init__( self.hidden_size = hidden_size self.num_heads = num_heads self.head_dim = hidden_size // num_heads - self.scale = self.head_dim ** -0.5 + self.scale = self.head_dim**-0.5 self.attention_mask_type = attention_mask_type # Projection layers self.q_proj = nn.Linear(hidden_size, hidden_size, bias=use_bias) diff --git a/code/models/cells/transformer/layers/readouts/AllGlobalAddPooling.py b/code/models/cells/transformer/layers/readouts/AllGlobalAddPooling.py index 44e4d6e..ef2d8f0 100644 --- a/code/models/cells/transformer/layers/readouts/AllGlobalAddPooling.py +++ b/code/models/cells/transformer/layers/readouts/AllGlobalAddPooling.py @@ -10,9 +10,9 @@ def forward( self, x: dict[int, Float[torch.Tensor, "..."], ...], x_belongings: dict[int, list[int]], - ) -> dict[int, Float[torch.Tensor, "..."], ...] | Float[ - torch.Tensor, "..." - ]: + ) -> ( + dict[int, Float[torch.Tensor, "..."], ...] | Float[torch.Tensor, "..."] + ): # Concatenate all x tensors sorted_x_indices = sorted(list(x.keys())) x_concat = torch.cat([x[i] for i in sorted_x_indices], dim=0) diff --git a/code/models/cells/transformer/layers/readouts/BaseReadout.py b/code/models/cells/transformer/layers/readouts/BaseReadout.py index 9a35afe..a82bf14 100644 --- a/code/models/cells/transformer/layers/readouts/BaseReadout.py +++ b/code/models/cells/transformer/layers/readouts/BaseReadout.py @@ -17,9 +17,9 @@ def forward( self, x: dict[int, Float[torch.Tensor, "..."], ...], x_belongings: dict[int, list[int]], - ) -> dict[int, Float[torch.Tensor, "..."], ...] | Float[ - torch.Tensor, "..." - ]: + ) -> ( + dict[int, Float[torch.Tensor, "..."], ...] | Float[torch.Tensor, "..."] + ): """ :param x: Dict of signals for the different dimensions. The keys are the dimensions. :param x_belongings: Dict of signal belongings diff --git a/code/models/cells/transformer/layers/readouts/GlobalAddPooling.py b/code/models/cells/transformer/layers/readouts/GlobalAddPooling.py index 16a1eae..c4c9d31 100644 --- a/code/models/cells/transformer/layers/readouts/GlobalAddPooling.py +++ b/code/models/cells/transformer/layers/readouts/GlobalAddPooling.py @@ -10,9 +10,9 @@ def forward( self, x: dict[int, Float[torch.Tensor, "..."], ...], x_belongings: dict[int, list[int]], - ) -> dict[int, Float[torch.Tensor, "..."], ...] | Float[ - torch.Tensor, "..." - ]: + ) -> ( + dict[int, Float[torch.Tensor, "..."], ...] | Float[torch.Tensor, "..."] + ): readout_result = dict() for key in x.keys(): readout_result[key] = pyg_nn.global_add_pool( diff --git a/code/models/cells/transformer/layers/readouts/GlobalBasicCombinationPooling.py b/code/models/cells/transformer/layers/readouts/GlobalBasicCombinationPooling.py index 7b39453..8d24139 100644 --- a/code/models/cells/transformer/layers/readouts/GlobalBasicCombinationPooling.py +++ b/code/models/cells/transformer/layers/readouts/GlobalBasicCombinationPooling.py @@ -28,9 +28,9 @@ def forward( self, x: dict[int, Float[torch.Tensor, "..."], ...], x_belongings: dict[int, list[int]], - ) -> dict[int, Float[torch.Tensor, "..."], ...] | Float[ - torch.Tensor, "..." - ]: + ) -> ( + dict[int, Float[torch.Tensor, "..."], ...] | Float[torch.Tensor, "..."] + ): readout_result = dict() max_pooling_result = self.global_max_pooling(x, x_belongings) add_pooling_result = self.global_add_pooling(x, x_belongings) diff --git a/code/models/cells/transformer/layers/readouts/GlobalMaxPooling.py b/code/models/cells/transformer/layers/readouts/GlobalMaxPooling.py index 475cc69..fb8e1eb 100644 --- a/code/models/cells/transformer/layers/readouts/GlobalMaxPooling.py +++ b/code/models/cells/transformer/layers/readouts/GlobalMaxPooling.py @@ -10,9 +10,9 @@ def forward( self, x: dict[int, Float[torch.Tensor, "..."], ...], x_belongings: dict[int, list[int]], - ) -> dict[int, Float[torch.Tensor, "..."], ...] | Float[ - torch.Tensor, "..." - ]: + ) -> ( + dict[int, Float[torch.Tensor, "..."], ...] | Float[torch.Tensor, "..."] + ): readout_result = dict() for key in x.keys(): readout_result[key] = pyg_nn.global_max_pool( diff --git a/code/models/cells/transformer/layers/readouts/GlobalMeanPooling.py b/code/models/cells/transformer/layers/readouts/GlobalMeanPooling.py index 1574f19..f2fc77b 100644 --- a/code/models/cells/transformer/layers/readouts/GlobalMeanPooling.py +++ b/code/models/cells/transformer/layers/readouts/GlobalMeanPooling.py @@ -10,9 +10,9 @@ def forward( self, x: dict[int, Float[torch.Tensor, "..."], ...], x_belongings: dict[int, list[int]], - ) -> dict[int, Float[torch.Tensor, "..."], ...] | Float[ - torch.Tensor, "..." - ]: + ) -> ( + dict[int, Float[torch.Tensor, "..."], ...] | Float[torch.Tensor, "..."] + ): readout_result = dict() for key in x.keys(): readout_result[key] = pyg_nn.global_mean_pool( diff --git a/code/models/cells/transformer/layers/readouts/NoReadout.py b/code/models/cells/transformer/layers/readouts/NoReadout.py index faf9ea2..508060e 100644 --- a/code/models/cells/transformer/layers/readouts/NoReadout.py +++ b/code/models/cells/transformer/layers/readouts/NoReadout.py @@ -13,9 +13,9 @@ def forward( self, x: dict[int, Float[torch.Tensor, "..."], ...], x_belongings: dict[int, list[int]], - ) -> dict[int, Float[torch.Tensor, "..."], ...] | Float[ - torch.Tensor, "..." - ]: + ) -> ( + dict[int, Float[torch.Tensor, "..."], ...] | Float[torch.Tensor, "..."] + ): if self.only_faces: return x[2] return x diff --git a/code/models/cells/transformer/layers/readouts/Set2SetPooling.py b/code/models/cells/transformer/layers/readouts/Set2SetPooling.py index 0befd0e..9b2b7d0 100644 --- a/code/models/cells/transformer/layers/readouts/Set2SetPooling.py +++ b/code/models/cells/transformer/layers/readouts/Set2SetPooling.py @@ -22,9 +22,9 @@ def forward( self, x: dict[int, Float[torch.Tensor, "..."], ...], x_belongings: dict[int, list[int]], - ) -> dict[int, Float[torch.Tensor, "..."], ...] | Float[ - torch.Tensor, "..." - ]: + ) -> ( + dict[int, Float[torch.Tensor, "..."], ...] | Float[torch.Tensor, "..."] + ): # Concatenate all x tensors sorted_x_indices = sorted(list(x.keys())) x_concat = torch.cat([x[i] for i in sorted_x_indices], dim=0) diff --git a/code/models/cells/transformer/positional_encodings/PositionalEncodings.py b/code/models/cells/transformer/positional_encodings/PositionalEncodings.py index b5b4eec..7ee16d4 100644 --- a/code/models/cells/transformer/positional_encodings/PositionalEncodings.py +++ b/code/models/cells/transformer/positional_encodings/PositionalEncodings.py @@ -8,9 +8,15 @@ BarycentricSubdivisionRandomWalkPE, BarycentricSubdivisionEigenvectorsPE, ) -from models.cells.transformer.positional_encodings.HodgeLaplacianEigenvectors import HodgeLaplacianEigenvectorsPE -from models.cells.transformer.positional_encodings.RandomWalks import RandomWalkPE -from models.cells.transformer.positional_encodings.ZeroPositionalEncodings import ZeroPE +from models.cells.transformer.positional_encodings.HodgeLaplacianEigenvectors import ( + HodgeLaplacianEigenvectorsPE, +) +from models.cells.transformer.positional_encodings.RandomWalks import ( + RandomWalkPE, +) +from models.cells.transformer.positional_encodings.ZeroPositionalEncodings import ( + ZeroPE, +) class PositionalEncodingsType(Enum): @@ -22,9 +28,9 @@ class PositionalEncodingsType(Enum): def get_positional_encodings( - t_complex: CellComplex | SimplicialComplex, - pe_type: PositionalEncodingsType, - length_positional_encodings: int, + t_complex: CellComplex | SimplicialComplex, + pe_type: PositionalEncodingsType, + length_positional_encodings: int, ) -> dict[int, Float[torch.Tensor, "n_dim length_positional_encodings"]]: match pe_type: case PositionalEncodingsType.HODGE_LAPLACIAN_EIGENVECTORS: @@ -33,12 +39,17 @@ def get_positional_encodings( pe_builder = ZeroPE() case PositionalEncodingsType.RANDOM_WALKS: pe_builder = RandomWalkPE() - case PositionalEncodingsType.BARYCENTRIC_SUBDIVISION_RANDOM_WALKS_GRAPH: + case ( + PositionalEncodingsType.BARYCENTRIC_SUBDIVISION_RANDOM_WALKS_GRAPH + ): pe_builder = BarycentricSubdivisionRandomWalkPE() - case PositionalEncodingsType.BARYCENTRIC_SUBDIVISION_GRAPH_EIGENVECTORS: + case ( + PositionalEncodingsType.BARYCENTRIC_SUBDIVISION_GRAPH_EIGENVECTORS + ): pe_builder = BarycentricSubdivisionEigenvectorsPE() case _: raise ValueError("Non-recognized positional encoding type") return pe_builder.generate_positional_encodings( - t_complex=t_complex, length_positional_encodings=length_positional_encodings + t_complex=t_complex, + length_positional_encodings=length_positional_encodings, ) diff --git a/code/results_betti_numbers.csv b/code/results_betti_numbers.csv deleted file mode 100644 index 97451c9..0000000 --- a/code/results_betti_numbers.csv +++ 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