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RoseTTAFoldModel.py
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import torch
import torch.nn as nn
from Embeddings import MSA_emb, Extra_emb, Templ_emb, Recycling, Timestep_emb
from Track_module import IterativeSimulator
from AuxiliaryPredictor import DistanceNetwork, MaskedTokenNetwork, ExpResolvedNetwork, LDDTNetwork
from util import INIT_CRDS
from opt_einsum import contract as einsum
from icecream import ic
class RoseTTAFoldModule(nn.Module):
def __init__(self,
n_extra_block,
n_main_block,
n_ref_block,
d_msa,
d_msa_full,
d_pair,
d_templ,
n_head_msa,
n_head_pair,
n_head_templ,
d_hidden,
d_hidden_templ,
p_drop,
d_t1d,
d_t2d,
d_time_emb, # total dims for input timestep emb
d_time_emb_proj, # size of projected timestep emb
T, # total timesteps (used in timestep emb
use_motif_timestep, # Whether to have a distinct emb for motif
freeze_track_motif, # Whether to freeze updates to motif in track
SE3_param_full={'l0_in_features':32, 'l0_out_features':16, 'num_edge_features':32},
SE3_param_topk={'l0_in_features':32, 'l0_out_features':16, 'num_edge_features':32},
input_seq_onehot=False, # For continuous vs. discrete sequence
):
super(RoseTTAFoldModule, self).__init__()
self.freeze_track_motif = freeze_track_motif
# Input Embeddings
d_state = SE3_param_topk['l0_out_features']
self.latent_emb = MSA_emb(d_msa=d_msa, d_pair=d_pair, d_state=d_state,
p_drop=p_drop, input_seq_onehot=input_seq_onehot) # Allowed to take onehotseq
self.full_emb = Extra_emb(d_msa=d_msa_full, d_init=25,
p_drop=p_drop, input_seq_onehot=input_seq_onehot) # Allowed to take onehotseq
self.templ_emb = Templ_emb(d_pair=d_pair, d_templ=d_templ, d_state=d_state,
n_head=n_head_templ,
d_hidden=d_hidden_templ, p_drop=0.25, d_t1d=d_t1d, d_t2d=d_t2d)
# timestep embedder
if d_time_emb:
print('NOTE: Using sinusoidal timestep embeddings of dim ',d_time_emb, ' projected to dim ',d_time_emb_proj)
assert d_t1d >= 22 + d_time_emb_proj, 'timestep projection size doesn\'t fit into RF t1d projection layers'
self.timestep_embedder = Timestep_emb(input_size=d_time_emb,
output_size=d_time_emb_proj,
T=T,
use_motif_timestep=use_motif_timestep)
# Update inputs with outputs from previous round
self.recycle = Recycling(d_msa=d_msa, d_pair=d_pair, d_state=d_state)
#
self.simulator = IterativeSimulator(n_extra_block=n_extra_block,
n_main_block=n_main_block,
n_ref_block=n_ref_block,
d_msa=d_msa, d_msa_full=d_msa_full,
d_pair=d_pair, d_hidden=d_hidden,
n_head_msa=n_head_msa,
n_head_pair=n_head_pair,
SE3_param_full=SE3_param_full,
SE3_param_topk=SE3_param_topk,
p_drop=p_drop)
##
self.c6d_pred = DistanceNetwork(d_pair, p_drop=p_drop)
self.aa_pred = MaskedTokenNetwork(d_msa, p_drop=p_drop)
self.lddt_pred = LDDTNetwork(d_state)
self.exp_pred = ExpResolvedNetwork(d_msa, d_state)
def forward(self, msa_latent, msa_full, seq, xyz, idx, t,
t1d=None, t2d=None, xyz_t=None, alpha_t=None,
msa_prev=None, pair_prev=None, state_prev=None,
return_raw=False, return_full=False, return_infer=False,
use_checkpoint=False, motif_mask=None, i_cycle=None, n_cycle=None):
B, N, L = msa_latent.shape[:3]
# Get embeddings
msa_latent, pair, state = self.latent_emb(msa_latent, seq, idx)
msa_full = self.full_emb(msa_full, seq, idx)
# Do recycling
if msa_prev == None:
msa_prev = torch.zeros_like(msa_latent[:,0])
pair_prev = torch.zeros_like(pair)
state_prev = torch.zeros_like(state)
msa_recycle, pair_recycle, state_recycle = self.recycle(seq, msa_prev, pair_prev, xyz, state_prev)
msa_latent[:,0] = msa_latent[:,0] + msa_recycle.reshape(B,L,-1)
pair = pair + pair_recycle
state = state + state_recycle
# Get timestep embedding (if using)
if hasattr(self, 'timestep_embedder'):
assert t is not None
time_emb = self.timestep_embedder(L,t,motif_mask)
n_tmpl = t1d.shape[1]
t1d = torch.cat([t1d, time_emb[None,None,...].repeat(1,n_tmpl,1,1)], dim=-1)
# add template embedding
pair, state = self.templ_emb(t1d, t2d, alpha_t, xyz_t, pair, state, use_checkpoint=use_checkpoint)
# Predict coordinates from given inputs
is_frozen_residue = motif_mask if self.freeze_track_motif else torch.zeros_like(motif_mask).bool()
msa, pair, R, T, alpha_s, state = self.simulator(seq, msa_latent, msa_full, pair, xyz[:,:,:3],
state, idx, use_checkpoint=use_checkpoint,
motif_mask=is_frozen_residue)
if return_raw:
# get last structure
xyz = einsum('bnij,bnaj->bnai', R[-1], xyz[:,:,:3]-xyz[:,:,1].unsqueeze(-2)) + T[-1].unsqueeze(-2)
return msa[:,0], pair, xyz, state, alpha_s[-1]
# predict masked amino acids
logits_aa = self.aa_pred(msa)
# Predict LDDT
lddt = self.lddt_pred(state)
if return_infer:
# get last structure
xyz = einsum('bnij,bnaj->bnai', R[-1], xyz[:,:,:3]-xyz[:,:,1].unsqueeze(-2)) + T[-1].unsqueeze(-2)
# get scalar plddt
nbin = lddt.shape[1]
bin_step = 1.0 / nbin
lddt_bins = torch.linspace(bin_step, 1.0, nbin, dtype=lddt.dtype, device=lddt.device)
pred_lddt = nn.Softmax(dim=1)(lddt)
pred_lddt = torch.sum(lddt_bins[None,:,None]*pred_lddt, dim=1)
return msa[:,0], pair, xyz, state, alpha_s[-1], logits_aa.permute(0,2,1), pred_lddt
#
# predict distogram & orientograms
logits = self.c6d_pred(pair)
# predict experimentally resolved or not
logits_exp = self.exp_pred(msa[:,0], state)
# get all intermediate bb structures
xyz = einsum('rbnij,bnaj->rbnai', R, xyz[:,:,:3]-xyz[:,:,1].unsqueeze(-2)) + T.unsqueeze(-2)
return logits, logits_aa, logits_exp, xyz, alpha_s, lddt