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# Copyright 2024 ByteDance and/or its affiliates.
#
# 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 Linear
# Adapted From openfold.model.heads
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)
def forward(self, z: torch.Tensor) -> torch.Tensor: # [*, N, N, C_z]
"""
Args:
z (torch.Tensor): pair embedding
[*, 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
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