File size: 6,863 Bytes
4d20b62 | 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 | # Copyright 2025 ByteDance and/or its affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import json
import os
from pathlib import Path
import numpy as np
import torch
from biotite.structure import AtomArray
from protenix.data.utils import save_atoms_to_cif
from protenix.utils.file_io import save_json
from protenix.utils.torch_utils import round_values
def get_clean_full_confidence(full_confidence_dict: dict) -> dict:
"""
Clean and format the full confidence dictionary by removing unnecessary keys and rounding values.
Args:
full_confidence_dict (dict): The dictionary containing full confidence data.
Returns:
dict: The cleaned and formatted dictionary.
"""
# Remove atom_coordinate
full_confidence_dict.pop("atom_coordinate")
# Remove atom_is_polymer
full_confidence_dict.pop("atom_is_polymer")
# Keep two decimal places
full_confidence_dict = round_values(full_confidence_dict)
return full_confidence_dict
class DataDumper:
def __init__(self, base_dir) -> None:
self.base_dir = base_dir
def dump(
self,
dataset_name: str,
pdb_id: str,
seed: int,
pred_dict: dict,
atom_array: AtomArray,
entity_poly_type: dict[str, str],
):
"""
Dump the predictions and related data to the specified directory.
Args:
dataset_name (str): The name of the dataset.
pdb_id (str): The PDB ID of the sample.
seed (int): The seed used for randomization.
pred_dict (dict): The dictionary containing the predictions.
atom_array (AtomArray): The AtomArray object containing the structure data.
entity_poly_type (dict[str, str]): The entity poly type information.
"""
dump_dir = self._get_dump_dir(dataset_name, pdb_id, seed)
Path(dump_dir).mkdir(parents=True, exist_ok=True)
self.dump_predictions(
pred_dict=pred_dict,
dump_dir=dump_dir,
pdb_id=pdb_id,
atom_array=atom_array,
entity_poly_type=entity_poly_type,
)
def _get_dump_dir(self, dataset_name: str, sample_name: str, seed: int) -> str:
"""
Generate the directory path for dumping data based on the dataset name, sample name, and seed.
"""
dump_dir = os.path.join(
self.base_dir, dataset_name, sample_name, f"seed_{seed}"
)
return dump_dir
def dump_predictions(
self,
pred_dict: dict,
dump_dir: str,
pdb_id: str,
atom_array: AtomArray,
entity_poly_type: dict[str, str],
):
"""
Dump raw predictions from the model:
structure: Save the predicted coordinates as CIF files.
confidence: Save the confidence data as JSON files.
"""
prediction_save_dir = os.path.join(dump_dir, "predictions")
os.makedirs(prediction_save_dir, exist_ok=True)
self._save_structure(
pred_dict["coordinate"],
prediction_save_dir,
pdb_id,
atom_array,
entity_poly_type,
)
self._save_confidence(
data=pred_dict, prediction_save_dir=prediction_save_dir, sample_name=pdb_id
)
self._mark_task_complete(dump_dir)
def _mark_task_complete(self, dump_dir):
success_file_path = os.path.join(dump_dir, f"SUCCESS_FILE")
success_data = {"prediction": True}
with open(success_file_path, "w") as f:
json.dump(success_data, f)
def check_completion(self, dataset_name, sample_name, seed):
dump_dir = self._get_dump_dir(dataset_name, sample_name, seed)
success_file_path = os.path.join(dump_dir, f"SUCCESS_FILE") # json file
return os.path.exists(success_file_path)
def _save_structure(
self,
pred_coordinates,
prediction_save_dir,
sample_name,
atom_array,
entity_poly_type=None,
):
N_sample = pred_coordinates.shape[0]
for sample_idx in range(N_sample):
output_fpath = os.path.join(
prediction_save_dir, f"{sample_name}_sample_{sample_idx}.cif"
)
# fake b_factor
atom_array.set_annotation(
"b_factor", np.round(np.zeros(len(atom_array)).astype(float), 2)
)
if "occupancy" not in atom_array._annot:
# fake occupancy
atom_array.set_annotation(
"occupancy", np.round(np.ones(len(atom_array)), 2)
)
save_structure_cif(
atom_array,
pred_coordinates[sample_idx],
output_fpath,
entity_poly_type,
sample_name,
# save_wounresol=False,
)
def _save_confidence(
self,
data: dict,
prediction_save_dir: str,
sample_name: str,
):
N_sample = (
len(data["summary_confidence"]) if "summary_confidence" in data else 0
)
if N_sample <= 0:
return
for idx, rank in enumerate(range(N_sample)):
output_fpath = os.path.join(
prediction_save_dir,
f"{sample_name}_summary_confidence_sample_{rank}.json",
)
save_json(data["summary_confidence"][idx], output_fpath, indent=4)
def save_structure_cif(
atom_array: AtomArray,
pred_coordinate: torch.Tensor,
output_fpath: str,
entity_poly_type: dict[str, str],
pdb_id: str,
):
"""
Save the predicted structure to a CIF file.
Args:
atom_array (AtomArray): The original AtomArray containing the structure.
pred_coordinate (torch.Tensor): The predicted coordinates for the structure.
output_fpath (str): The output file path for saving the CIF file.
entity_poly_type (dict[str, str]): The entity poly type information.
pdb_id (str): The PDB ID for the entry.
"""
pred_atom_array = copy.deepcopy(atom_array)
pred_pose = pred_coordinate.cpu().numpy()
pred_atom_array.coord = pred_pose
save_atoms_to_cif(
output_fpath,
pred_atom_array,
entity_poly_type,
pdb_id,
)
|