import sys, os import torch from icecream import ic import random import numpy as np from kinematics import get_init_xyz sys.path.append('../') from utils.calc_dssp import annotate_sse ic.configureOutput(includeContext=True) def mask_inputs(seq, msa_masked, msa_full, xyz_t, t1d, mask_msa, input_seq_mask=None, input_str_mask=None, input_floating_mask=None, input_t1dconf_mask=None, loss_seq_mask=None, loss_str_mask=None, loss_str_mask_2d=None, dssp=False, hotspots=False, diffuser=None, t=None, freeze_seq_emb=False, mutate_seq=False, no_clamp_seq=False, norm_input=False, contacts=None, frac_provide_dssp=0.5, dssp_mask_percentage=[0,100], frac_provide_contacts=0.5, struc_cond=False): """ Parameters: seq (torch.tensor, required): (I,L) integer sequence msa_masked (torch.tensor, required): (I,N_short,L,48) msa_full (torch,.tensor, required): (I,N_long,L,25) xyz_t (torch,tensor): (T,L,27,3) template crds BEFORE they go into get_init_xyz t1d (torch.tensor, required): (I,L,22) this is the t1d before tacking on the chi angles str_mask_1D (torch.tensor, required): Shape (L) rank 1 tensor where structure is masked at False positions seq_mask_1D (torch.tensor, required): Shape (L) rank 1 tensor where seq is masked at False positions t1d_24: is there an extra dimension to input structure confidence? diffuser: diffuser class t: time step NOTE: in the MSA, the order is 20aa, 1x unknown, 1x mask token. We set the masked region to 22 (masked). For the t1d, this has 20aa, 1x unkown, and 1x template conf. Here, we set the masked region to 21 (unknown). This, we think, makes sense, as the template in normal RF training does not perfectly correspond to the MSA. """ #ic(input_seq_mask.shape) #ic(seq.shape) #ic(msa_masked.shape) #ic(msa_full.shape) #ic(t1d.shape) #ic(xyz_t.shape) #ic(input_str_mask.shape) #ic(mask_msa.shape) ########### seq_mask = input_seq_mask ###################### ###sequence diffusion### ###################### str_mask = input_str_mask x_0 = torch.nn.functional.one_hot(seq[0,...],num_classes=22).float()*2-1 seq_diffused = diffuser.q_sample(x_0,t,mask=seq_mask) seq_tmp=torch.argmax(seq_diffused,axis=-1).to(device=seq.device) seq=seq_tmp.repeat(seq.shape[0], 1) ################### ###msa diffusion### ################### ### msa_masked ### #ic(msa_masked.shape) B,N,L,_=msa_masked.shape msa_masked[:,0,:,:22] = seq_diffused x_0_msa = msa_masked[0,1:,:,:22].float()*2-1 msa_seq_mask = seq_mask.unsqueeze(0).repeat(N-1, 1) msa_diffused = diffuser.q_sample(x_0_msa,torch.tensor([t]),mask=msa_seq_mask) msa_masked[:,1:,:,:22] = torch.clone(msa_diffused) # index 44/45 is insertion/deletion # index 43 is the masked token NOTE check this # index 42 is the unknown token msa_masked[:,0,:,22:44] = seq_diffused msa_masked[:,1:,:,22:44] = msa_diffused # insertion/deletion stuff msa_masked[:,0,~seq_mask,44:46] = 0 ### msa_full ### ################ #make msa_full same size as msa_masked #ic(msa_full.shape) msa_full = msa_full[:,:msa_masked.shape[1],:,:] msa_full[:,0,:,:22] = seq_diffused msa_full[:,1:,:,:22] = msa_diffused ### t1d ### ########### # NOTE: adjusting t1d last dim (confidence) from sequence mask t1d = torch.cat((t1d, torch.zeros((t1d.shape[0],t1d.shape[1],1)).float()), -1).to(seq.device) t1d[:,:,:21] = seq_diffused[...,:21] #t1d[:,:,21] *= input_t1dconf_mask #set diffused conf to 0 and everything else to 1 t1d[:,~seq_mask,21] = 0.0 t1d[:,seq_mask,21] = 1.0 t1d[:1,:,22] = 1-t/diffuser.num_timesteps #to do add structure confidence metric; need to expand dimensions of chkpt b4 #if t1d_24: JG - changed to be default t1d = torch.cat((t1d, torch.zeros((t1d.shape[0],t1d.shape[1],1)).float()), -1).to(seq.device) t1d[:,~str_mask,23] = 0.0 t1d[:,str_mask,23] = 1.0 if dssp: print(f'adding dssp {frac_provide_dssp} of time') t1d = torch.cat((t1d, torch.zeros((t1d.shape[0],t1d.shape[1],4)).float()), -1).to(seq.device) #dssp info #mask some percentage of dssp info in range dssp_mask_percentage[0],dssp_mask_percentage[1] percentage_mask=random.randint(dssp_mask_percentage[0], dssp_mask_percentage[1]) dssp=annotate_sse(np.array(xyz_t[0,:,1,:].squeeze()), percentage_mask=percentage_mask) #dssp_unmasked = annotate_sse(np.array(xyz_t[0,:,1,:].squeeze()), percentage_mask=0) if np.random.rand()>frac_provide_dssp: print('masking dssp') dssp[...]=0 #replace with mask token dssp[:,-1]=1 t1d[...,24:]=dssp if hotspots: print(f"adding hotspots {frac_provide_contacts} of time") t1d = torch.cat((t1d, torch.zeros((t1d.shape[0],t1d.shape[1],1)).float()), -1).to(seq.device) #mask all contacts some fraction of the time if np.random.rand()>frac_provide_contacts: print('masking contacts') contacts = torch.zeros(L) t1d[...,-1] = contacts ### xyz_t ### ############# xyz_t = get_init_xyz(xyz_t[None]) xyz_t = xyz_t[0] #Sequence masking xyz_t[:,:,3:,:] = float('nan') # Structure masking if struc_cond: print("non-autoregressive structure conditioning") r = diffuser.alphas_cumprod[t] xyz_mask = (torch.rand(xyz_t.shape[1]) > r).to(torch.bool).to(seq.device) xyz_mask = torch.logical_and(xyz_mask,~str_mask) xyz_t[:,xyz_mask,:,:] = float('nan') else: xyz_t[:,~str_mask,:,:] = float('nan') ### mask_msa ### ################ # NOTE: this is for loss scoring mask_msa[:,:,~loss_seq_mask] = False out=dict( seq= seq, msa_masked= msa_masked, msa_full= msa_full, xyz_t= xyz_t, t1d= t1d, mask_msa= mask_msa, seq_diffused= seq_diffused ) return out