diffalign.models.encoder package

Submodules

diffalign.models.encoder.edge module

class diffalign.models.encoder.edge.MLPEdgeEncoder(hidden_dim=100, activation='relu')

Bases: Module

forward(edge_length, edge_type)

Input:

edge_length: The length of edges, shape=(E, 1). edge_type: The type pf edges, shape=(E,)

Returns:

The representation of edges. (E, self.hidden_dim)

Return type:

edge_attr

property out_channels

diffalign.models.encoder.egnn module

class diffalign.models.encoder.egnn.EGNN(in_node_nf: int, in_edge_nf: int, hidden_nf: int, device: str = 'cpu', act_fn=SiLU(), n_layers: int = 4, attention: bool = False, norm_diff: bool = True, out_node_nf: int | None = None, tanh: bool = False, coords_range: float = 15.0, agg: str = 'sum', norm_constant: float = 0.0)

Bases: Module

forward(h: Tensor, x: Tensor, edges: Tensor, edge_attr: Tensor | None = None, node_mask: Tensor | None = None, edge_mask: Tensor | None = None, coord_mask: Tensor | None = None, node_attr: Tensor | None = None)

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class diffalign.models.encoder.egnn.E_GCL(input_nf: int, output_nf: int, hidden_nf: int, edges_in_d: int = 0, nodes_att_dim: int = 0, act_fn=SiLU(), attention: bool = False, norm_diff: bool = True, tanh: bool = False, coords_range: float = 1.0, norm_constant: float = 0.0)

Bases: Module

coord2radial(edge_index: Tensor, coord: Tensor)

coord_model(coord, edge_index, coord_diff, edge_feat, edge_mask, coord_mask)

edge_model(source, target, radial, edge_attr, edge_mask)

forward(h: Tensor, edge_index: Tensor, coord: Tensor, edge_attr: Tensor | None = None, node_attr: Tensor | None = None, node_mask: Tensor | None = None, edge_mask: Tensor | None = None, coord_mask: Tensor | None = None)

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

node_model(x, edge_index, edge_feat, node_attr)

diffalign.models.encoder.egnn.unsorted_segment_sum(data: Tensor, segment_ids: Tensor, num_segments: int) → Tensor

TensorFlow-style unsorted_segment_sum for 2D tensors [E, C].

Module contents