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import torch.nn as nn
from src.utils.license_register import register_license
from src.model.modules.primitives import Linear
from src.utils.data.constants import BOND_TYPE
from src.api.model_interface import (
PairFormerOutput,
PairwiseOutput,
)
@register_license('bytedance2024')
class DistogramHead(nn.Module):
"""Implements Algorithm 1 [Line17] in AF3
Computes a distogram probability distribution.
For use in computation of distogram loss, subsection 1.9.8 (AF2)
"""
def __init__(self, c_z: int = 128, no_bins: int = 64) -> None:
"""
Args:
c_z (int, optional): hidden dim [for pair embedding]. Defaults to 128.
no_bins (int, optional): Number of distogram bins. Defaults to 64.
"""
super(DistogramHead, self).__init__()
self.c_z = c_z
self.no_bins = no_bins
self.linear = Linear(
in_features=self.c_z, out_features=self.no_bins, initializer="zeros"
)
def forward(self, z: torch.Tensor) -> torch.Tensor: # [*, N, N, C_z]
"""
Args:
z (torch.Tensor): pairformer output
[*, N_token, N_token, C_z]
Returns:
torch.Tensor: distogram probability distribution
[*, N_token, N_token, no_bins]
"""
# [*, N, N, no_bins]
logits = self.linear(z)
logits = logits + logits.transpose(-2, -3)
return logits
@register_license('odesign2025')
class BondTypeHead(nn.Module):
"""Implements a bond type classifier head.
Computes bond type probability distribution.
"""
def __init__(self, c_z: int = 1, c_hidden: int = 128, no_bond_types: int = len(BOND_TYPE)) -> None:
"""
Args:
c_in (int, optional): hidden dim [for pair embedding]. Defaults to 1.
c_hidden (int, optional): hidden dim. Defaults to 128.
no_bond_types (int, optional): Number of bond types. Defaults to len(BOND_TYPE).
"""
super(BondTypeHead, self).__init__()
self.c_in = c_z
self.c_hidden = c_hidden
self.no_bond_types = no_bond_types
self.classifier = nn.Sequential(
Linear(self.c_in, self.c_hidden),
Linear(self.c_hidden, no_bond_types),
)
def forward(self, z: torch.Tensor) -> torch.Tensor:
"""
Args:
z (torch.Tensor): pairformer output
[*, N_token, N_token, c_z]
Returns:
torch.Tensor: bond type probability distribution
[*, N_token, N_token, no_bond_types]
"""
# [*, N_token, N_token, no_bond_types]
logits = self.classifier(z.unsqueeze(0))
return logits
@register_license('odesign2025')
class PairwiseHead(nn.Module):
"""Implements a pairwise head.
Computes pairwise probability distribution.
"""
def __init__(
self,
c_z: int = 128,
no_bond_types: int = len(BOND_TYPE),
no_bins: int = 64,
bond_reconstruction = True
) -> PairwiseOutput:
"""
Args:
c_in (int, optional): hidden dim [for pair embedding]. Defaults to 1.
c_hidden (int, optional): hidden dim. Defaults to 128.
no_bond_types (int, optional): Number of bond types. Defaults to len(BOND_TYPE).
"""
super(PairwiseHead, self).__init__()
self.distogram_head = DistogramHead(c_z=c_z, no_bins=no_bins)
self.bond_reconstruction = bond_reconstruction
if bond_reconstruction:
self.bond_type_head = BondTypeHead(c_z=c_z, no_bond_types=no_bond_types)
def forward(self, input_embedding: PairFormerOutput) -> PairwiseOutput:
"""
Args:
input (PairFormerOutput): pairformer output
[*, N_token, N_token, c_z]
Returns:
PairwiseOutput: bond type probability distribution
[*, N_token, N_token, no_bond_types]
"""
# [*, N_token, N_token, no_bond_types]
distogram = self.distogram_head(input_embedding.z)
if self.bond_reconstruction:
token_bond_type_logits = self.bond_type_head(input_embedding.z)
else:
token_bond_type_logits = None
return PairwiseOutput(distogram, token_bond_type_logits) |