wehub-resource-sync
118 lines
4.3 KiB
Python
118 lines
4.3 KiB
Python
import dgl
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import dgl.function as fn
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import torch.nn as nn
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from modules.initializers import GlorotOrthogonal
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from modules.residual_layer import ResidualLayer
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class InteractionPPBlock(nn.Module):
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def __init__(
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self,
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emb_size,
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int_emb_size,
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basis_emb_size,
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num_radial,
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num_spherical,
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num_before_skip,
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num_after_skip,
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activation=None,
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):
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super(InteractionPPBlock, self).__init__()
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self.activation = activation
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# Transformations of Bessel and spherical basis representations
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self.dense_rbf1 = nn.Linear(num_radial, basis_emb_size, bias=False)
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self.dense_rbf2 = nn.Linear(basis_emb_size, emb_size, bias=False)
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self.dense_sbf1 = nn.Linear(
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num_radial * num_spherical, basis_emb_size, bias=False
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)
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self.dense_sbf2 = nn.Linear(basis_emb_size, int_emb_size, bias=False)
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# Dense transformations of input messages
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self.dense_ji = nn.Linear(emb_size, emb_size)
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self.dense_kj = nn.Linear(emb_size, emb_size)
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# Embedding projections for interaction triplets
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self.down_projection = nn.Linear(emb_size, int_emb_size, bias=False)
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self.up_projection = nn.Linear(int_emb_size, emb_size, bias=False)
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# Residual layers before skip connection
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self.layers_before_skip = nn.ModuleList(
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[
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ResidualLayer(emb_size, activation=activation)
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for _ in range(num_before_skip)
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]
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)
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self.final_before_skip = nn.Linear(emb_size, emb_size)
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# Residual layers after skip connection
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self.layers_after_skip = nn.ModuleList(
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[
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ResidualLayer(emb_size, activation=activation)
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for _ in range(num_after_skip)
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]
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)
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self.reset_params()
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def reset_params(self):
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GlorotOrthogonal(self.dense_rbf1.weight)
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GlorotOrthogonal(self.dense_rbf2.weight)
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GlorotOrthogonal(self.dense_sbf1.weight)
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GlorotOrthogonal(self.dense_sbf2.weight)
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GlorotOrthogonal(self.dense_ji.weight)
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nn.init.zeros_(self.dense_ji.bias)
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GlorotOrthogonal(self.dense_kj.weight)
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nn.init.zeros_(self.dense_kj.bias)
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GlorotOrthogonal(self.down_projection.weight)
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GlorotOrthogonal(self.up_projection.weight)
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def edge_transfer(self, edges):
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# Transform from Bessel basis to dense vector
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rbf = self.dense_rbf1(edges.data["rbf"])
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rbf = self.dense_rbf2(rbf)
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# Initial transformation
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x_ji = self.dense_ji(edges.data["m"])
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x_kj = self.dense_kj(edges.data["m"])
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if self.activation is not None:
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x_ji = self.activation(x_ji)
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x_kj = self.activation(x_kj)
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x_kj = self.down_projection(x_kj * rbf)
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if self.activation is not None:
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x_kj = self.activation(x_kj)
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return {"x_kj": x_kj, "x_ji": x_ji}
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def msg_func(self, edges):
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sbf = self.dense_sbf1(edges.data["sbf"])
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sbf = self.dense_sbf2(sbf)
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x_kj = edges.src["x_kj"] * sbf
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return {"x_kj": x_kj}
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def forward(self, g, l_g):
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g.apply_edges(self.edge_transfer)
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# nodes correspond to edges and edges correspond to nodes in the original graphs
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# node: d, rbf, o, rbf_env, x_kj, x_ji
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for k, v in g.edata.items():
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l_g.ndata[k] = v
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l_g_reverse = dgl.reverse(l_g, copy_edata=True)
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l_g_reverse.update_all(self.msg_func, fn.sum("x_kj", "m_update"))
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g.edata["m_update"] = self.up_projection(l_g_reverse.ndata["m_update"])
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if self.activation is not None:
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g.edata["m_update"] = self.activation(g.edata["m_update"])
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# Transformations before skip connection
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g.edata["m_update"] = g.edata["m_update"] + g.edata["x_ji"]
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for layer in self.layers_before_skip:
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g.edata["m_update"] = layer(g.edata["m_update"])
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g.edata["m_update"] = self.final_before_skip(g.edata["m_update"])
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if self.activation is not None:
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g.edata["m_update"] = self.activation(g.edata["m_update"])
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# Skip connection
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g.edata["m"] = g.edata["m"] + g.edata["m_update"]
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# Transformations after skip connection
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for layer in self.layers_after_skip:
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g.edata["m"] = layer(g.edata["m"])
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return g
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