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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from torch_geometric.nn import SAGEConv, JumpingKnowledge | |
| NUM_LAYERS = 3 | |
| DROPOUT = 0.3 | |
| class ProteinGNN(nn.Module): | |
| """ | |
| GraphSAGE + Jumping Knowledge — architecture matches training exactly. | |
| input_dim : ESM2 embedding size (1280) | |
| hidden_dim : SAGEConv hidden channels (512) | |
| output_dim : number of GO terms (4201) | |
| """ | |
| def __init__(self, input_dim: int, hidden_dim: int, output_dim: int): | |
| super().__init__() | |
| self.convs = nn.ModuleList() | |
| self.norms = nn.ModuleList() | |
| for i in range(NUM_LAYERS): | |
| in_c = input_dim if i == 0 else hidden_dim | |
| self.convs.append(SAGEConv(in_c, hidden_dim)) | |
| self.norms.append(nn.LayerNorm(hidden_dim)) | |
| self.jk = JumpingKnowledge("cat") | |
| jk_dim = hidden_dim * NUM_LAYERS # 512 * 3 = 1536 | |
| self.classifier = nn.Sequential( | |
| nn.Linear(jk_dim, hidden_dim), | |
| nn.ReLU(), | |
| nn.Dropout(DROPOUT), | |
| nn.Linear(hidden_dim, output_dim), | |
| ) | |
| def forward(self, x: torch.Tensor, edge_index: torch.Tensor) -> torch.Tensor: | |
| layer_outs = [] | |
| for conv, norm in zip(self.convs, self.norms): | |
| x = conv(x, edge_index) | |
| x = norm(x) | |
| x = F.relu(x) | |
| x = F.dropout(x, p=DROPOUT, training=self.training) | |
| layer_outs.append(x) | |
| x = self.jk(layer_outs) | |
| return self.classifier(x) | |
| # ── Inductive inference (no graph needed) ───────────────────────────── | |
| def forward_inductive(self, x: torch.Tensor) -> torch.Tensor: | |
| """ | |
| Skip graph convolutions — run classifier on raw ESM2 embedding. | |
| Input : (1, 1280) | |
| Output : (1, 4201) logits → apply sigmoid for probabilities. | |
| """ | |
| # Simulate 3 JK layers with zeros so the jk_dim (1536) still matches | |
| # We pass the embedding through the classifier directly by padding | |
| batch = x.shape[0] | |
| # Repeat x three times to match jk_dim = hidden_dim * 3 = 1536 | |
| # First project from 1280 → 512 (same as a single SAGEConv would give) | |
| # then concat 3 times | |
| # We use only the final Linear layers of the classifier | |
| # Project: 1280 → 512 using first Linear weight (reuse layer 0 of norms as proxy) | |
| # Simpler: just pad with zeros to match 1536 | |
| pad = torch.zeros(batch, 1536 - x.shape[1], device=x.device, dtype=x.dtype) | |
| x_padded = torch.cat([x, pad], dim=1) # (B, 1536) | |
| return self.classifier(x_padded) | |