Datasets:
File size: 13,481 Bytes
84a242a | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 | """
Cache Manager for Persistent Storage of Processed Protein Graphs
This module provides thread-safe persistent caching functionality for protein graphs,
maintaining subdirectory structure and supporting efficient lookups.
"""
import os
import torch
import threading
import hashlib
from pathlib import Path
from typing import Optional, Dict, Any
from collections import OrderedDict
import time
class CacheManager:
"""Persistent cache manager for protein graph data with multiprocessing support."""
def __init__(self, cache_path: str, max_size: int = 100000):
"""
Initialize cache manager.
Args:
cache_path: Directory to store cached files
max_size: Maximum number of items to keep in memory cache
"""
self.cache_path = Path(cache_path)
self.cache_root = self.cache_path # Add missing cache_root attribute
self.max_size = max_size
self.cache_path.mkdir(parents=True, exist_ok=True)
# Memory cache with LRU eviction
self._memory_cache = OrderedDict()
self._lock = threading.Lock()
# Stats
self._hits = 0
self._misses = 0
print(f"CacheManager: Initialized with cache_path={cache_path}, max_size={max_size}")
def __getstate__(self):
"""Custom pickling - exclude lock from state."""
state = self.__dict__.copy()
# Remove unpickleable lock
del state['_lock']
return state
def __setstate__(self, state):
"""Custom unpickling - restore state and create new lock."""
self.__dict__.update(state)
# Create new lock in worker process
self._lock = threading.Lock()
def _get_cache_path(self, pdb_file_path: str, maintain_structure: bool = False) -> Path:
"""
Get cache file path for a given PDB file.
Args:
pdb_file_path: Original PDB file path
maintain_structure: Whether to maintain subdirectory structure
Returns:
Path to cache file
"""
pdb_path = Path(pdb_file_path)
if maintain_structure:
# Maintain relative subdirectory structure
# Extract relative path from dataset root
try:
# Try to find 'dataset' in the path
path_parts = pdb_path.parts
if 'dataset' in path_parts:
dataset_idx = path_parts.index('dataset')
# Get everything after 'dataset' except the filename
rel_parts = path_parts[dataset_idx + 1:-1]
cache_subdir = self.cache_root / Path(*rel_parts)
else:
# Fallback: use parent directory name
cache_subdir = self.cache_root / pdb_path.parent.name
except (ValueError, IndexError):
# Fallback: use flattened structure
cache_subdir = self.cache_root
else:
# Flattened structure
cache_subdir = self.cache_root
# Create subdirectory if it doesn't exist
cache_subdir.mkdir(parents=True, exist_ok=True)
pdb_ccd_id = '_'.join(pdb_path.stem.split('_')[:-1])
# Generate cache filename: pyg_graph_{original_stem}.pt
cache_filename = f"pyg_graph_{pdb_ccd_id}_esmc_600m.pt"
return cache_subdir / cache_filename
def _get_file_hash(self, file_path: str) -> Optional[str]:
"""
Get SHA256 hash of file for validation (optional, since PDB files are immutable).
Args:
file_path: Path to file
Returns:
SHA256 hash or None if file doesn't exist
"""
try:
with open(file_path, 'rb') as f:
file_hash = hashlib.sha256(f.read()).hexdigest()
return file_hash
except (IOError, OSError):
return None
def exists(self, pdb_file_path: str) -> bool:
"""
Check if cached graph exists for given PDB file.
Args:
pdb_file_path: Original PDB file path
Returns:
True if cache exists, False otherwise
"""
cache_path = self._get_cache_path(pdb_file_path)
return cache_path.exists()
def load(self, pdb_file_path: str):
"""
Load cached graph data.
Args:
pdb_file_path: Original PDB file path
Returns:
Graph data (Data object or dictionary) or None if not found/invalid
"""
with self._lock:
try:
cache_path = self._get_cache_path(pdb_file_path)
if not cache_path.exists():
return None
# Load cached data
graph_data = torch.load(cache_path, map_location='cpu')
# Validate cached data structure
from torch_geometric.data import Data
if isinstance(graph_data, Data):
# Validate Data object
if graph_data.x is None or graph_data.edge_index is None or graph_data.pos is None:
print(f"Cache Manager: Invalid cached Data object for {pdb_file_path}, removing cache file")
cache_path.unlink() # Remove invalid cache file
return None
else:
# Legacy dictionary format validation
required_keys = ['node_features', 'edge_index', 'node_pos']
if not all(key in graph_data for key in required_keys):
print(f"Cache Manager: Invalid cached data for {pdb_file_path}, removing cache file")
cache_path.unlink() # Remove invalid cache file
return None
# Ensure edge_attr exists for dictionary format
if 'edge_attr' not in graph_data:
num_edges = graph_data['edge_index'].shape[1]
graph_data['edge_attr'] = torch.ones((num_edges, 1))
print(f"Cache Manager: Loaded cached graph for {pdb_file_path}")
return graph_data
except Exception as e:
print(f"Cache Manager: Error loading cache for {pdb_file_path}: {e}")
# Try to remove corrupted cache file
try:
cache_path = self._get_cache_path(pdb_file_path)
if cache_path.exists():
cache_path.unlink()
print(f"Cache Manager: Removed corrupted cache file: {cache_path}")
except:
pass
return None
def save(self, pdb_file_path: str, graph_data: Dict[str, torch.Tensor]) -> bool:
"""
Save graph data to cache.
Args:
pdb_file_path: Original PDB file path
graph_data: Graph data dictionary to cache
Returns:
True if saved successfully, False otherwise
"""
with self._lock:
try:
cache_path = self._get_cache_path(pdb_file_path)
# Validate data before saving
from torch_geometric.data import Data
# Check if it's a PyTorch Geometric Data object
if isinstance(graph_data, Data):
# Validate Data object has required attributes
if graph_data.x is None or graph_data.edge_index is None or graph_data.pos is None:
print(f"Cache Manager: Invalid Data object, missing required attributes, cannot cache {pdb_file_path}")
return False
else:
# Legacy dictionary format validation
required_keys = ['node_features', 'edge_index', 'node_pos']
if not all(key in graph_data for key in required_keys):
print(f"Cache Manager: Invalid graph data, cannot cache {pdb_file_path}")
return False
# Save to temporary file first, then rename (atomic operation)
temp_path = cache_path.with_suffix('.tmp')
torch.save(graph_data, temp_path)
temp_path.rename(cache_path)
print(f"Cache Manager: Cached graph for {pdb_file_path} -> {cache_path}")
return True
except Exception as e:
print(f"Cache Manager: Error saving cache for {pdb_file_path}: {e}")
# Clean up temporary file if it exists
try:
temp_path = self._get_cache_path(pdb_file_path).with_suffix('.tmp')
if temp_path.exists():
temp_path.unlink()
except:
pass
return False
def remove(self, pdb_file_path: str) -> bool:
"""
Remove cached graph data.
Args:
pdb_file_path: Original PDB file path
Returns:
True if removed successfully, False otherwise
"""
with self._lock:
try:
cache_path = self._get_cache_path(pdb_file_path)
if cache_path.exists():
cache_path.unlink()
print(f"Cache Manager: Removed cache for {pdb_file_path}")
return True
except Exception as e:
print(f"Cache Manager: Error removing cache for {pdb_file_path}: {e}")
return False
def clear_all(self) -> int:
"""
Clear all cached data.
Returns:
Number of files removed
"""
with self._lock:
removed_count = 0
try:
for cache_file in self.cache_root.rglob("*.pt"):
try:
cache_file.unlink()
removed_count += 1
except:
pass
print(f"Cache Manager: Cleared {removed_count} cache files")
except Exception as e:
print(f"Cache Manager: Error clearing cache: {e}")
return removed_count
def get_cache_stats(self) -> Dict[str, Any]:
"""
Get cache statistics.
Returns:
Dictionary with cache statistics
"""
try:
cache_files = list(self.cache_root.rglob("*.pt"))
total_size = sum(f.stat().st_size for f in cache_files if f.exists())
return {
'cache_root': str(self.cache_root),
'num_cached_files': len(cache_files),
'total_size_bytes': total_size,
'total_size_mb': total_size / (1024 * 1024),
'subdirectories': len(list(self.cache_root.rglob("*/")))
}
except Exception as e:
print(f"Cache Manager: Error getting stats: {e}")
return {
'cache_root': str(self.cache_root),
'num_cached_files': 0,
'total_size_bytes': 0,
'total_size_mb': 0,
'subdirectories': 0,
'error': str(e)
}
def validate_cache(self) -> Dict[str, int]:
"""
Validate all cached files and remove corrupted ones.
Returns:
Dictionary with validation results
"""
with self._lock:
stats = {'valid': 0, 'invalid': 0, 'removed': 0}
try:
for cache_file in self.cache_root.rglob("*.pt"):
try:
# Try to load the file
graph_data = torch.load(cache_file, map_location='cpu')
# Validate structure
required_keys = ['node_features', 'edge_index', 'node_pos']
if all(key in graph_data for key in required_keys):
stats['valid'] += 1
else:
stats['invalid'] += 1
cache_file.unlink()
stats['removed'] += 1
print(f"Cache Manager: Removed invalid cache file: {cache_file}")
except Exception:
stats['invalid'] += 1
try:
cache_file.unlink()
stats['removed'] += 1
print(f"Cache Manager: Removed corrupted cache file: {cache_file}")
except:
pass
except Exception as e:
print(f"Cache Manager: Error during validation: {e}")
print(f"Cache Manager: Validation complete - Valid: {stats['valid']}, Invalid: {stats['invalid']}, Removed: {stats['removed']}")
return stats
|