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# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn as nn
from torch.nn import Parameter
from dockformer.model.primitives import Linear, LayerNorm
from dockformer.utils.loss import (
compute_plddt,
compute_tm,
compute_predicted_aligned_error,
)
from dockformer.utils.precision_utils import is_fp16_enabled
class AuxiliaryHeads(nn.Module):
def __init__(self, config):
super(AuxiliaryHeads, self).__init__()
self.plddt = PerResidueLDDTCaPredictor(
**config["lddt"],
)
self.distogram = DistogramHead(
**config["distogram"],
)
self.affinity_2d = Affinity2DPredictor(
**config["affinity_2d"],
)
self.affinity_1d = Affinity1DPredictor(
**config["affinity_1d"],
)
self.affinity_cls = AffinityClsTokenPredictor(
**config["affinity_cls"],
)
self.binding_site = BindingSitePredictor(
**config["binding_site"],
)
self.inter_contact = InterContactHead(
**config["inter_contact"],
)
self.config = config
def forward(self, outputs, inter_mask, affinity_mask):
aux_out = {}
lddt_logits = self.plddt(outputs["sm"]["single"])
aux_out["lddt_logits"] = lddt_logits
# Required for relaxation later on
aux_out["plddt"] = compute_plddt(lddt_logits)
distogram_logits = self.distogram(outputs["pair"])
aux_out["distogram_logits"] = distogram_logits
aux_out["inter_contact_logits"] = self.inter_contact(outputs["single"], outputs["pair"])
aux_out["affinity_2d_logits"] = self.affinity_2d(outputs["pair"], aux_out["inter_contact_logits"], inter_mask)
aux_out["affinity_1d_logits"] = self.affinity_1d(outputs["single"])
aux_out["affinity_cls_logits"] = self.affinity_cls(outputs["single"], affinity_mask)
aux_out["binding_site_logits"] = self.binding_site(outputs["single"])
return aux_out
class Affinity2DPredictor(nn.Module):
def __init__(self, c_z, num_bins):
super(Affinity2DPredictor, self).__init__()
self.c_z = c_z
self.weight_linear = Linear(self.c_z + 1, 1)
self.embed_linear = Linear(self.c_z, self.c_z)
self.bins_linear = Linear(self.c_z, num_bins)
def forward(self, z, inter_contacts_logits, inter_pair_mask):
z_with_inter_contacts = torch.cat((z, inter_contacts_logits), dim=-1) # [*, N, N, c_z + 1]
weights = self.weight_linear(z_with_inter_contacts) # [*, N, N, 1]
x = self.embed_linear(z) # [*, N, N, c_z]
batch_size, N, M, _ = x.shape
flat_weights = weights.reshape(batch_size, N*M, -1) # [*, N*M, 1]
flat_x = x.reshape(batch_size, N*M, -1) # [*, N*M, c_z]
flat_inter_pair_mask = inter_pair_mask.reshape(batch_size, N*M, 1)
flat_weights = flat_weights.masked_fill(~(flat_inter_pair_mask.bool()), float('-inf')) # [*, N*N, 1]
flat_weights = torch.nn.functional.softmax(flat_weights, dim=1) # [*, N*N, 1]
flat_weights = torch.nan_to_num(flat_weights, nan=0.0) # [*, N*N, 1]
weighted_sum = torch.sum((flat_weights * flat_x).reshape(batch_size, N*M, -1), dim=1) # [*, c_z]
return self.bins_linear(weighted_sum)
class Affinity1DPredictor(nn.Module):
def __init__(self, c_s, num_bins, **kwargs):
super(Affinity1DPredictor, self).__init__()
self.c_s = c_s
self.linear1 = Linear(self.c_s, self.c_s, init="final")
self.linear2 = Linear(self.c_s, num_bins, init="final")
def forward(self, s):
# [*, N, C_out]
s = self.linear1(s)
# get an average over the sequence
s = torch.mean(s, dim=1)
logits = self.linear2(s)
return logits
class AffinityClsTokenPredictor(nn.Module):
def __init__(self, c_s, num_bins, **kwargs):
super(AffinityClsTokenPredictor, self).__init__()
self.c_s = c_s
self.linear = Linear(self.c_s, num_bins, init="final")
def forward(self, s, affinity_mask):
affinity_tokens = (s * affinity_mask.unsqueeze(-1)).sum(dim=1)
return self.linear(affinity_tokens)
class BindingSitePredictor(nn.Module):
def __init__(self, c_s, c_out, **kwargs):
super(BindingSitePredictor, self).__init__()
self.c_s = c_s
self.c_out = c_out
self.linear = Linear(self.c_s, self.c_out, init="final")
def forward(self, s):
# [*, N, C_out]
return self.linear(s)
class InterContactHead(nn.Module):
def __init__(self, c_s, c_z, c_out, **kwargs):
"""
Args:
c_z:
Input channel dimension
c_out:
Number of bins, but since boolean should be 1
"""
super(InterContactHead, self).__init__()
self.c_s = c_s
self.c_z = c_z
self.c_out = c_out
self.linear = Linear(2 * self.c_s + self.c_z, self.c_out, init="final")
def forward(self, s, z): # [*, N, N, C_z]
# [*, N, N, no_bins]
batch_size, n, s_dim = s.shape
s_i = s.unsqueeze(2).expand(batch_size, n, n, s_dim)
s_j = s.unsqueeze(1).expand(batch_size, n, n, s_dim)
joined = torch.cat((s_i, s_j, z), dim=-1)
logits = self.linear(joined)
return logits
class PerResidueLDDTCaPredictor(nn.Module):
def __init__(self, no_bins, c_in, c_hidden):
super(PerResidueLDDTCaPredictor, self).__init__()
self.no_bins = no_bins
self.c_in = c_in
self.c_hidden = c_hidden
self.layer_norm = LayerNorm(self.c_in)
self.linear_1 = Linear(self.c_in, self.c_hidden, init="relu")
self.linear_2 = Linear(self.c_hidden, self.c_hidden, init="relu")
self.linear_3 = Linear(self.c_hidden, self.no_bins, init="final")
self.relu = nn.ReLU()
def forward(self, s):
s = self.layer_norm(s)
s = self.linear_1(s)
s = self.relu(s)
s = self.linear_2(s)
s = self.relu(s)
s = self.linear_3(s)
return s
class DistogramHead(nn.Module):
"""
Computes a distogram probability distribution.
For use in computation of distogram loss, subsection 1.9.8
"""
def __init__(self, c_z, no_bins, **kwargs):
"""
Args:
c_z:
Input channel dimension
no_bins:
Number of distogram bins
"""
super(DistogramHead, self).__init__()
self.c_z = c_z
self.no_bins = no_bins
self.linear = Linear(self.c_z, self.no_bins, init="final")
def _forward(self, z): # [*, N, N, C_z]
"""
Args:
z:
[*, N_res, N_res, C_z] pair embedding
Returns:
[*, N, N, no_bins] distogram probability distribution
"""
# [*, N, N, no_bins]
logits = self.linear(z)
logits = logits + logits.transpose(-2, -3)
return logits
def forward(self, z):
if(is_fp16_enabled()):
with torch.cuda.amp.autocast(enabled=False):
return self._forward(z.float())
else:
return self._forward(z)
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