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sampling.py
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sampling.py
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"""
https://github.com/ProteinDesignLab/protpardelle
License: MIT
Author: Alex Chu
Configs and convenience functions for wrapping the model sample() function.
"""
import argparse
import time
from typing import Optional, Tuple
import torch
from torchtyping import TensorType
from core import residue_constants
from core import utils
import diffusion
def default_backbone_sampling_config():
config = argparse.Namespace(
n_steps=500,
s_churn=200,
step_scale=1.2,
sidechain_mode=False,
noise_schedule=lambda t: diffusion.noise_schedule(t, s_max=80, s_min=0.001),
)
return config
def default_allatom_sampling_config():
noise_schedule = lambda t: diffusion.noise_schedule(t, s_max=80, s_min=0.001)
stage2 = argparse.Namespace(
apply_cond_proportion=1.0,
n_steps=200,
s_churn=100,
step_scale=1.2,
sidechain_mode=True,
skip_mpnn_proportion=1.0,
noise_schedule=noise_schedule,
)
config = argparse.Namespace(
n_steps=500,
s_churn=200,
step_scale=1.2,
sidechain_mode=True,
skip_mpnn_proportion=0.6,
use_fullmpnn=False,
use_fullmpnn_for_final=True,
anneal_seq_resampling_rate="linear",
noise_schedule=noise_schedule,
stage_2=stage2,
)
return config
def draw_backbone_samples(
model: torch.nn.Module,
seq_mask: TensorType["b n", float] = None,
n_samples: int = None,
sample_length_range: Tuple[int] = (50, 512),
pdb_save_path: Optional[str] = None,
return_aux: bool = False,
return_sampling_runtime: bool = False,
**sampling_kwargs,
):
device = model.device
if seq_mask is None:
assert n_samples is not None
seq_mask = model.make_seq_mask_for_sampling(
n_samples=n_samples,
min_len=sample_length_range[0],
max_len=sample_length_range[1],
)
start = time.time()
aux = model.sample(
seq_mask=seq_mask, return_last=False, return_aux=True, **sampling_kwargs
)
aux["runtime"] = time.time() - start
seq_lens = seq_mask.sum(-1).long()
cropped_samp_coords = [
s[: seq_lens[i], model.bb_idxs] for i, s in enumerate(aux["xt_traj"][-1])
]
if pdb_save_path is not None:
gly_aatype = (seq_mask * residue_constants.restype_order["G"]).long()
trimmed_aatype = [a[: seq_lens[i]] for i, a in enumerate(gly_aatype)]
atom_mask = utils.atom37_mask_from_aatype(gly_aatype, seq_mask).cpu()
for i in range(len(cropped_samp_coords)):
utils.write_coords_to_pdb(
cropped_samp_coords[i],
f"{pdb_save_path}{i}.pdb",
batched=False,
aatype=trimmed_aatype[i],
atom_mask=atom_mask[i],
)
if return_aux:
return aux
else:
if return_sampling_runtime:
return cropped_samp_coords, seq_mask, aux["runtime"]
else:
return cropped_samp_coords, seq_mask
def draw_allatom_samples(
model: torch.nn.Module,
seq_mask: TensorType["b n", float] = None,
n_samples: int = None,
sample_length_range: Tuple[int] = (50, 512),
two_stage_sampling: bool = True,
pdb_save_path: Optional[str] = None,
return_aux: bool = False,
return_sampling_runtime: bool = False,
**sampling_kwargs,
):
"""Implement the default 2-stage all-atom sampling routine."""
def save_allatom_samples(aux, path):
seq_lens = aux["seq_mask"].sum(-1).long()
cropped_samp_coords = [
c[: seq_lens[i]] for i, c in enumerate(aux["xt_traj"][-1])
]
cropped_samp_aatypes = [
s[: seq_lens[i]] for i, s in enumerate(aux["st_traj"][-1])
]
samp_atom_mask = utils.atom37_mask_from_aatype(
aux["st_traj"][-1].to(device), seq_mask
)
samp_atom_mask = [m[: seq_lens[i]] for i, m in enumerate(samp_atom_mask)]
for i, c in enumerate(cropped_samp_coords):
utils.write_coords_to_pdb(
c,
f"{path}{i}.pdb",
batched=False,
aatype=cropped_samp_aatypes[i],
atom_mask=samp_atom_mask[i],
conect=True,
)
device = model.device
if seq_mask is None:
assert n_samples is not None
seq_mask = model.make_seq_mask_for_sampling(
n_samples=n_samples,
min_len=sample_length_range[0],
max_len=sample_length_range[1],
)
sampling_runtime = 0.0
# Stage 1 sampling
start = time.time()
if "stage_2" in sampling_kwargs:
stage_2_kwargs = vars(sampling_kwargs.pop("stage_2"))
aux = model.sample(
seq_mask=seq_mask,
return_last=False,
return_aux=True,
**sampling_kwargs,
)
sampling_runtime = time.time() - start
if pdb_save_path is not None and two_stage_sampling:
save_allatom_samples(aux, pdb_save_path + "_init")
# Stage 2 sampling (sidechain refinement only)
if two_stage_sampling:
samp_seq = aux["st_traj"][-1]
samp_coords = aux["xt_traj"][-1]
cond_atom_mask = utils.atom37_mask_from_aatype((seq_mask * 7).long(), seq_mask)
aux = {f"stage1_{k}": v for k, v in aux.items()}
start = time.time()
stage2_aux = model.sample(
gt_cond_atom_mask=cond_atom_mask.to(device), # condition on backbone
gt_cond_seq_mask=seq_mask.to(device),
gt_coords=samp_coords.to(device),
gt_aatype=samp_seq.to(device),
seq_mask=seq_mask,
return_last=False,
return_aux=True,
**stage_2_kwargs,
)
sampling_runtime += time.time() - start
aux = {**aux, **stage2_aux}
if pdb_save_path is not None:
save_allatom_samples(aux, pdb_save_path + "_samp")
aux["runtime"] = sampling_runtime
# Process outputs, crop to correct length
if return_aux:
return aux
else:
xt_traj = aux["xt_traj"]
st_traj = aux["st_traj"]
seq_mask = aux["seq_mask"]
seq_lens = seq_mask.sum(-1).long()
cropped_samp_coords = [c[: seq_lens[i]] for i, c in enumerate(xt_traj[-1])]
cropped_samp_aatypes = [s[: seq_lens[i]] for i, s in enumerate(st_traj[-1])]
samp_atom_mask = utils.atom37_mask_from_aatype(st_traj[-1].to(device), seq_mask)
samp_atom_mask = [m[: seq_lens[i]] for i, m in enumerate(samp_atom_mask)]
orig_xt_traj = aux["stage1_xt_traj"]
stage1_coords = [c[: seq_lens[i]] for i, c in enumerate(orig_xt_traj[-1])]
ret = (
cropped_samp_coords,
cropped_samp_aatypes,
samp_atom_mask,
stage1_coords,
seq_mask,
)
if return_sampling_runtime:
ret = ret + (sampling_runtime,)
return ret