Spaces:
Running
on
T4
Running
on
T4
File size: 5,859 Bytes
59a9ccf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 |
import torch
import torch.nn as nn
from Embeddings import MSA_emb, Extra_emb, Templ_emb, Recycling
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=4, n_main_block=8, n_ref_block=4,\
d_msa=256, d_msa_full=64, d_pair=128, d_templ=64,
n_head_msa=8, n_head_pair=4, n_head_templ=4,
d_hidden=32, d_hidden_templ=64,
p_drop=0.15, d_t1d=24, d_t2d=44,
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},
):
super(RoseTTAFoldModule, self).__init__()
#
# 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)
self.full_emb = Extra_emb(d_msa=d_msa_full, d_init=25, p_drop=p_drop)
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)
# 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,
seq1hot=None, 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,
use_checkpoint=False, return_infer=False):
B, N, L = msa_latent.shape[:3]
# Get embeddings
#ic(seq.shape)
#ic(msa_latent.shape)
#ic(seq1hot.shape)
#ic(idx.shape)
#ic(xyz.shape)
#ic(seq1hot.shape)
#ic(t1d.shape)
#ic(t2d.shape)
idx = idx.long()
msa_latent, pair, state = self.latent_emb(msa_latent, seq, idx, seq1hot=seq1hot)
msa_full = self.full_emb(msa_full, seq, idx, seq1hot=seq1hot)
#
# Do recycling
if msa_prev == None:
msa_prev = torch.zeros_like(msa_latent[:,0])
if pair_prev == None:
pair_prev = torch.zeros_like(pair)
if state_prev == None:
state_prev = torch.zeros_like(state)
#ic(seq.shape)
#ic(msa_prev.shape)
#ic(pair_prev.shape)
#ic(xyz.shape)
#ic(state_prev.shape)
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
#
#ic(t1d.dtype)
#ic(t2d.dtype)
#ic(alpha_t.dtype)
#ic(xyz_t.dtype)
#ic(pair.dtype)
#ic(state.dtype)
#import pdb; pdb.set_trace()
# add template embedding
pair, state = self.templ_emb(t1d, t2d, alpha_t, xyz_t, pair, state, use_checkpoint=use_checkpoint)
#ic(seq.dtype)
#ic(msa_latent.dtype)
#ic(msa_full.dtype)
#ic(pair.dtype)
#ic(xyz.dtype)
#ic(state.dtype)
#ic(idx.dtype)
# Predict coordinates from given inputs
msa, pair, R, T, alpha_s, state = self.simulator(seq, msa_latent, msa_full.type(torch.float32), pair, xyz[:,:,:3],
state, idx, use_checkpoint=use_checkpoint)
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 distogram & orientograms
logits = self.c6d_pred(pair)
# Predict LDDT
lddt = self.lddt_pred(state)
# predict experimentally resolved or not
logits_exp = self.exp_pred(msa[:,0], state)
if return_infer:
#get last structure
xyz = einsum('bnij,bnaj->bnai', R[-1], xyz[:,:,:3]-xyz[:,:,1].unsqueeze(-2)) + T[-1].unsqueeze(-2)
return logits, logits_aa, logits_exp, xyz, lddt, msa[:,0], pair, state, alpha_s[-1]
# 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
|