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ca7299e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 | """PDB data loader."""
import math
import torch
import tree
import numpy as np
import torch
import pandas as pd
import logging
import os
import random
import esm
import copy
from data import utils as du
from data.repr import get_pre_repr
from openfold.data import data_transforms
from openfold.utils import rigid_utils
from data.residue_constants import restype_atom37_mask, order2restype_with_mask
from pytorch_lightning import LightningDataModule
from torch.utils.data import DataLoader, Dataset
from torch.utils.data.distributed import DistributedSampler, dist
from scipy.spatial.transform import Rotation as scipy_R
class PdbDataModule(LightningDataModule):
def __init__(self, data_cfg):
super().__init__()
self.data_cfg = data_cfg
self.loader_cfg = data_cfg.loader
self.dataset_cfg = data_cfg.dataset
self.sampler_cfg = data_cfg.sampler
def setup(self, stage: str):
self._train_dataset = PdbDataset(
dataset_cfg=self.dataset_cfg,
is_training=True,
)
self._valid_dataset = PdbDataset(
dataset_cfg=self.dataset_cfg,
is_training=False,
)
def train_dataloader(self, rank=None, num_replicas=None):
num_workers = self.loader_cfg.num_workers
return DataLoader( # default batch_size is 1, and it is expand in training_step of FlowModule
self._train_dataset,
# batch_sampler=LengthBatcher(
# sampler_cfg=self.sampler_cfg,
# metadata_csv=self._train_dataset.csv,
# rank=rank,
# num_replicas=num_replicas,
# ),
sampler=DistributedSampler(self._train_dataset, shuffle=True),
num_workers=self.loader_cfg.num_workers,
prefetch_factor=None if num_workers == 0 else self.loader_cfg.prefetch_factor,
persistent_workers=True if num_workers > 0 else False,
# persistent_workers=False,
)
def val_dataloader(self):
num_workers = self.loader_cfg.num_workers
return DataLoader(
self._valid_dataset,
sampler=DistributedSampler(self._valid_dataset, shuffle=False),
num_workers=self.loader_cfg.num_workers,
prefetch_factor=None if num_workers == 0 else self.loader_cfg.prefetch_factor,
persistent_workers=True,
# persistent_workers=False,
)
class PdbDataset(Dataset):
def __init__(
self,
*,
dataset_cfg,
is_training,
):
self._log = logging.getLogger(__name__)
self._is_training = is_training
self._dataset_cfg = dataset_cfg
self.split_frac = self._dataset_cfg.split_frac
self.random_seed = self._dataset_cfg.seed
# self.count = 0
self._init_metadata()
@property
def is_training(self):
return self._is_training
@property
def dataset_cfg(self):
return self._dataset_cfg
def _init_metadata(self):
"""Initialize metadata."""
# Process CSV with different filtering criterions.
pdb_csv = pd.read_csv(self.dataset_cfg.csv_path)
self.raw_csv = pdb_csv
pdb_csv = pdb_csv[pdb_csv.modeled_seq_len <= self.dataset_cfg.max_num_res]
pdb_csv = pdb_csv[pdb_csv.modeled_seq_len >= self.dataset_cfg.min_num_res]
if self.dataset_cfg.subset is not None:
pdb_csv = pdb_csv.iloc[:self.dataset_cfg.subset]
pdb_csv = pdb_csv.sort_values('modeled_seq_len', ascending=False)
# energy_csv_path = self.dataset_cfg.energy_csv_path
# self.energy_csv = pd.read_csv(energy_csv_path)
## Training or validation specific logic.
if self.is_training:
self.csv = pdb_csv[pdb_csv['is_trainset']]
self.csv = pdb_csv.sample(frac=self.split_frac, random_state=self.random_seed).reset_index()
self.csv.to_csv(os.path.join(os.path.dirname(self.dataset_cfg.csv_path),"train.csv"), index=False)
# self.chain_feats_total = []
# for idx in range(len(self.csv)):
# if idx % 200 == 0:
# self._log.info(f"pre_count= {idx}")
# # processed_path = self.csv.iloc[idx]['processed_path']
# # chain_feats_temp = self._process_csv_row(processed_path)
# # chain_feats_temp = du.read_pkl(processed_path)
# # chain_feats_temp['energy'] = torch.tensor(self.csv.iloc[idx]['energy'], dtype=torch.float32)
# self.chain_feats_total[idx]['energy'] = torch.tensor(self.csv.iloc[idx]['energy'], dtype=torch.float32)
# # self.chain_feats_total += [chain_feats_temp]
# self._log.info(
# f"Training: {len(self.chain_feats_total)} examples, len_range is {self.csv['modeled_seq_len'].min()}-{self.csv['modeled_seq_len'].max()}")
self._log.info(
f"Training: {len(self.csv)} examples, len_range is {self.csv['modeled_seq_len'].min()}-{self.csv['modeled_seq_len'].max()}")
else:
self.csv = pdb_csv[~pdb_csv['is_trainset']]
# if self.split_frac < 1.0:
# train_csv = pdb_csv.sample(frac=self.split_frac, random_state=self.random_seed)
# pdb_csv = pdb_csv.drop(train_csv.index)
self.csv = pdb_csv[pdb_csv.modeled_seq_len <= self.dataset_cfg.max_eval_length]
self.csv.to_csv(os.path.join(os.path.dirname(self.dataset_cfg.csv_path),"valid.csv"), index=False)
self.csv = self.csv.sample(n=min(self.dataset_cfg.max_valid_num, len(self.csv)), random_state=self.random_seed).reset_index()
# self.chain_feats_total = []
# for idx in range(len(self.csv)):
# processed_path = self.csv.iloc[idx]['processed_path']
# # chain_feats_temp = self._process_csv_row(processed_path)
# chain_feats_temp = du.read_pkl(processed_path)
# chain_feats_temp['energy'] = torch.tensor(self.csv.iloc[idx]['energy'], dtype=torch.float32)
# self.chain_feats_total += [chain_feats_temp]
# self._log.info(
# f"Valid: {len(self.chain_feats_total)} examples, len_range is {self.csv['modeled_seq_len'].min()}-{self.csv['modeled_seq_len'].max()}")
self._log.info(
f"Valid: {len(self.csv)} examples, len_range is {self.csv['modeled_seq_len'].min()}-{self.csv['modeled_seq_len'].max()}")
# def _process_csv_row(self, processed_file_path):
# self.count += 1
# if self.count%200==0:
# self._log.info(
# f"pre_count= {self.count}")
# output_total = du.read_pkl(processed_file_path)
# energy_csv = self.energy_csv
# file = os.path.basename(processed_file_path).replace(".pkl", ".pdb")
# matching_rows = energy_csv[energy_csv['traj_filename'] == file]
# if not matching_rows.empty:
# output_total['energy'] = torch.tensor(matching_rows['energy'].values[0], dtype=torch.float32)
# return output_total
def __len__(self):
return len(self.csv)
def __getitem__(self, idx):
# chain_feats = self.chain_feats_total[idx]
processed_path = self.csv.iloc[idx]['processed_path']
chain_feats = du.read_pkl(processed_path)
chain_feats['energy'] = torch.tensor(self.csv.iloc[idx]['energy'], dtype=torch.float32)
energy = chain_feats['energy']
if self.is_training and self._dataset_cfg.use_split:
# split_len = self._dataset_cfg.split_len
split_len = random.randint(self.dataset_cfg.min_num_res, min(self._dataset_cfg.split_len, chain_feats['aatype'].shape[0]))
idx = random.randint(0,chain_feats['aatype'].shape[0]-split_len)
output_total = copy.deepcopy(chain_feats)
output_total['energy'] = torch.ones(chain_feats['aatype'].shape)
output_temp = tree.map_structure(lambda x: x[idx:idx+split_len], output_total)
bb_center = np.sum(output_temp['bb_positions'], axis=0) / (np.sum(output_temp['res_mask'].numpy()) + 1e-5) # (3,)
output_temp['trans_1']=(output_temp['trans_1'] - torch.from_numpy(bb_center[None, :])).float()
output_temp['bb_positions']=output_temp['bb_positions']- bb_center[None, :]
output_temp['all_atom_positions']=output_temp['all_atom_positions'] - torch.from_numpy(bb_center[None, None, :])
output_temp['pair_repr_pre'] = output_temp['pair_repr_pre'][:,idx:idx+split_len]
bb_center_esmfold = torch.sum(output_temp['trans_esmfold'], dim=0) / (np.sum(output_temp['res_mask'].numpy()) + 1e-5) # (3,)
output_temp['trans_esmfold']=(output_temp['trans_esmfold'] - bb_center_esmfold[None, :]).float()
chain_feats = output_temp
chain_feats['energy'] = energy
if self._dataset_cfg.use_rotate_enhance:
rot_vet = [random.random() for _ in range(3)]
rot_mat = torch.tensor(scipy_R.from_rotvec(rot_vet).as_matrix()) # (3,3)
chain_feats['all_atom_positions']=torch.einsum('lij,kj->lik',chain_feats['all_atom_positions'],
rot_mat.type(chain_feats['all_atom_positions'].dtype))
all_atom_mask = np.array([restype_atom37_mask[i] for i in chain_feats['aatype']])
chain_feats_temp = {
'aatype': chain_feats['aatype'],
'all_atom_positions': chain_feats['all_atom_positions'],
'all_atom_mask': torch.tensor(all_atom_mask).double(),
}
chain_feats_temp = data_transforms.atom37_to_frames(chain_feats_temp)
curr_rigid = rigid_utils.Rigid.from_tensor_4x4(chain_feats_temp['rigidgroups_gt_frames'])[:, 0]
chain_feats['trans_1'] = curr_rigid.get_trans()
chain_feats['rotmats_1'] = curr_rigid.get_rots().get_rot_mats()
chain_feats['bb_positions']=(chain_feats['trans_1']).numpy().astype(chain_feats['bb_positions'].dtype)
return chain_feats
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