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from torch_geometric.data import Data, Dataset
from abc import ABC, abstractmethod
from typing import Tuple, List, Optional, Union
from pathlib import Path
import logging
from .embeddings import EmbeddingManager
from .pdb_utils import load_backbone_coordinates
import warnings
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
def generate_edges_no_self(num_nodes: int) -> Tuple[torch.Tensor, torch.Tensor]:
i, j = torch.meshgrid(
torch.arange(num_nodes), torch.arange(num_nodes), indexing="ij"
)
mask = i != j
return torch.vstack((i[mask], j[mask]))
def build_knn_edges(
dist_matrix: torch.Tensor, k: int
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
num_nodes = dist_matrix.size(0)
_, nearest_neighbors = torch.topk(dist_matrix, k=k + 1, largest=False)
nearest_neighbors = nearest_neighbors[:, 1:]
k = nearest_neighbors.size(1)
row_indices = (
torch.arange(num_nodes, device=dist_matrix.device)[:, None]
.expand(-1, k)
.flatten()
)
col_indices = nearest_neighbors.flatten()
mask = torch.ones(
(num_nodes, num_nodes), dtype=torch.bool, device=dist_matrix.device
)
mask[torch.arange(num_nodes), torch.arange(num_nodes)] = False
mask.scatter_(1, nearest_neighbors, False)
return row_indices, col_indices, mask
def build_random_edges(
num_nodes: int, mask: torch.Tensor, l: int
) -> tuple[torch.Tensor, torch.Tensor]:
rand_probs = torch.rand((num_nodes, num_nodes), device=mask.device).masked_fill(
~mask, -float("inf")
)
_, random_indices = torch.topk(rand_probs, l, dim=1)
row_indices = (
torch.arange(num_nodes, device=mask.device)[:, None].expand(-1, l).flatten()
)
col_indices = random_indices.flatten()
return row_indices, col_indices
def build_connectivity(
seq_len: int,
dist_matrix: torch.Tensor,
k_nearest: int,
l_random: int,
num_layers: int,
change_connectivity: bool,
) -> tuple[torch.Tensor, torch.Tensor]:
if k_nearest + l_random >= seq_len - 1:
row_index, col_index_fixed = generate_edges_no_self(seq_len)
if change_connectivity:
col_index = col_index_fixed.unsqueeze(0).repeat(num_layers, 1)
else:
col_index = col_index_fixed
return row_index, col_index
if k_nearest > 0:
row_index_knn, col_index_knn, mask = build_knn_edges(dist_matrix, k_nearest)
else:
mask = torch.ones((seq_len, seq_len), dtype=torch.bool)
mask.fill_diagonal_(False)
row_index_knn = torch.empty(0, dtype=torch.long)
col_index_knn = torch.empty(0, dtype=torch.long)
if not change_connectivity:
if l_random > 0:
row_index_rand, col_index_rand = build_random_edges(seq_len, mask, l_random)
else:
row_index_rand = torch.empty(0, dtype=torch.long)
col_index_rand = torch.empty(0, dtype=torch.long)
row_index_total = torch.cat([row_index_knn, row_index_rand], dim=0)
col_index_total = torch.cat([col_index_knn, col_index_rand], dim=0)
return row_index_total, col_index_total
else:
fixed_row = row_index_knn
fixed_col = col_index_knn
col_indices_layers = []
for _ in range(num_layers):
if l_random > 0:
row_index_rand_layer, col_index_rand_layer = build_random_edges(
seq_len, mask, l_random
)
else:
row_index_rand_layer = torch.empty(0, dtype=torch.long)
col_index_rand_layer = torch.empty(0, dtype=torch.long)
combined_col = torch.cat([fixed_col, col_index_rand_layer], dim=0)
col_indices_layers.append(combined_col)
if l_random > 0:
row_index_rand = row_index_rand_layer
else:
row_index_rand = torch.empty(0, dtype=torch.long)
total_row_index = torch.cat([fixed_row, row_index_rand], dim=0)
col_index = torch.stack(col_indices_layers, dim=0)
return total_row_index, col_index
class BaseDataset(Dataset, ABC):
def __init__(
self,
ground_truth_dir: Union[str, Path],
embedding_dir: Union[str, Path],
emb_model: str,
device: str,
k_nearest: int,
l_random: int,
num_layers: int,
change_connectivity: bool,
):
super().__init__()
self.ground_truth_dir = Path(ground_truth_dir)
self.embedding_dir = Path(embedding_dir)
self.emb_model = emb_model.lower()
self.device = device
self.k_nearest = k_nearest
self.l_random = l_random
self.num_layers = num_layers
self.change_connectivity = change_connectivity
def _build_connectivity(
self, ca_coords: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
dist_matrix = torch.cdist(ca_coords, ca_coords)
return build_connectivity(
seq_len=ca_coords.size(0),
dist_matrix=dist_matrix,
k_nearest=self.k_nearest,
l_random=self.l_random,
num_layers=self.num_layers,
change_connectivity=self.change_connectivity,
)
def _load_embedding(self, name: str, seq_len: int) -> torch.Tensor:
emb_path = self.embedding_dir / f"{name}_{self.emb_model}.pt"
if not emb_path.exists():
raise FileNotFoundError(f"Embedding {emb_path} not found")
emb = torch.load(emb_path, map_location="cpu", weights_only=True)
if emb.size(0) == seq_len + 2: # Handle BOS/EOS tokens
emb = emb[1:-1]
elif emb.size(0) != seq_len:
raise ValueError(f"Embedding size mismatch: {emb.size(0)} vs {seq_len}")
return emb
def _compute_embeddings(self, names: List[str], sequences: List[str]) -> None:
"""Shared logic for computing missing embeddings."""
if not names:
return
logger.info(f"Computing embeddings for {len(names)} samples")
EmbeddingManager(
embedding_dir=self.embedding_dir,
emb_model=self.emb_model,
device=self.device,
).get_or_compute_embeddings(names, sequences)
@abstractmethod
def __getitem__(self, idx: int) -> Data:
pass
@abstractmethod
def __len__(self) -> int:
pass
class InferenceDataset(BaseDataset):
def __init__(
self,
entries: List[str],
ground_truth_dir: Union[str, Path],
embedding_dir: Union[str, Path],
emb_model: str,
device: str,
k_nearest: int,
l_random: int,
num_layers: int,
change_connectivity: bool,
):
super().__init__(
ground_truth_dir=ground_truth_dir,
embedding_dir=embedding_dir,
emb_model=emb_model,
device=device,
k_nearest=k_nearest,
l_random=l_random,
num_layers=num_layers,
change_connectivity=change_connectivity,
)
self.file_paths = self._resolve_entries(entries)
self._compute_missing_embeddings()
def _resolve_entries(self, entries: List[str]) -> List[Path]:
resolved = []
for entry in entries:
path = Path(entry)
if path.exists():
resolved.append(path.resolve())
continue
for ext in [".pt"]:
candidate = self.ground_truth_dir / f"{entry}{ext}"
if candidate.exists():
resolved.append(candidate)
break
else:
logger.warning(f"Couldn't resolve entry: {entry}")
if not resolved:
raise ValueError("No valid input files found")
return resolved
def _compute_missing_embeddings(self) -> None:
valid_names, sequences = [], []
for path in self.file_paths:
emb_path = self.embedding_dir / f"{path.stem}_{self.emb_model}.pt"
if emb_path.exists():
continue
try:
sequences.append(self._extract_sequence(path))
valid_names.append(path.stem)
except Exception as e:
logger.error(f"Failed processing {path.name}: {e}")
self._compute_embeddings(valid_names, sequences)
def _extract_sequence(self, path: Path) -> str:
if path.suffix == ".pt":
return torch.load(path, map_location="cpu", weights_only=True).get(
"seq", ""
)
elif path.suffix == ".pdb":
seq = load_backbone_coordinates(str(path), allow_hetatm=True)["seq"]
return seq
raise ValueError(f"Unsupported file type: {path.suffix}")
def __len__(self) -> int:
return len(self.file_paths)
def __getitem__(self, idx: int) -> Data:
path = self.file_paths[idx]
if path.suffix == ".pt":
data = torch.load(path, map_location="cpu", weights_only=True)
bb, coverage = data["bb"], data.get("coverage", torch.ones(len(data["bb"])))
else:
bb = load_backbone_coordinates(str(path), allow_hetatm=True)["bb"]
coverage = torch.ones(len(bb))
row_idx, col_idx = self._build_connectivity(bb[:, 1])
return Data(
x=self._load_embedding(path.stem, bb.size(0)),
row_index=row_idx,
col_index=col_idx,
bb=bb,
coverage=coverage,
name=path.stem,
path=str(path),
)
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