more memory fixes hopeufly

memory_profiler_script.py

@@ -0,0 +1,227 @@
+#!/usr/bin/env python3
+"""
+Memory profiling script for VR180 matting pipeline
+Tracks memory usage during processing to identify leaks
+"""
+
+import sys
+import time
+import psutil
+import tracemalloc
+import subprocess
+import gc
+from pathlib import Path
+from typing import Dict, List, Tuple
+import threading
+import json
+
+class MemoryProfiler:
+    def __init__(self, output_file: str = "memory_profile.json"):
+        self.output_file = output_file
+        self.data = []
+        self.process = psutil.Process()
+        self.running = False
+        self.thread = None
+        
+    def start_monitoring(self, interval: float = 1.0):
+        """Start continuous memory monitoring"""
+        tracemalloc.start()
+        self.running = True
+        self.thread = threading.Thread(target=self._monitor_loop, args=(interval,))
+        self.thread.daemon = True
+        self.thread.start()
+        print(f"🔍 Memory monitoring started (interval: {interval}s)")
+        
+    def stop_monitoring(self):
+        """Stop monitoring and save results"""
+        self.running = False
+        if self.thread:
+            self.thread.join()
+        
+        # Get tracemalloc snapshot
+        snapshot = tracemalloc.take_snapshot()
+        top_stats = snapshot.statistics('lineno')
+        
+        # Save detailed results
+        results = {
+            'timeline': self.data,
+            'top_memory_allocations': [
+                {
+                    'file': stat.traceback.format()[0],
+                    'size_mb': stat.size / 1024 / 1024,
+                    'count': stat.count
+                }
+                for stat in top_stats[:20]  # Top 20 allocations
+            ],
+            'summary': {
+                'peak_rss_gb': max([d['rss_gb'] for d in self.data]) if self.data else 0,
+                'peak_vram_gb': max([d['vram_gb'] for d in self.data]) if self.data else 0,
+                'total_samples': len(self.data)
+            }
+        }
+        
+        with open(self.output_file, 'w') as f:
+            json.dump(results, f, indent=2)
+        
+        tracemalloc.stop()
+        print(f"📊 Memory profile saved to {self.output_file}")
+        
+    def _monitor_loop(self, interval: float):
+        """Continuous monitoring loop"""
+        while self.running:
+            try:
+                # System memory
+                memory_info = self.process.memory_info()
+                rss_gb = memory_info.rss / (1024**3)
+                
+                # System-wide memory
+                sys_memory = psutil.virtual_memory()
+                sys_used_gb = (sys_memory.total - sys_memory.available) / (1024**3)
+                sys_available_gb = sys_memory.available / (1024**3)
+                
+                # GPU memory (if available)
+                vram_gb = 0
+                vram_free_gb = 0
+                try:
+                    result = subprocess.run(['nvidia-smi', '--query-gpu=memory.used,memory.free', 
+                                           '--format=csv,noheader,nounits'], 
+                                          capture_output=True, text=True, timeout=5)
+                    if result.returncode == 0:
+                        lines = result.stdout.strip().split('\n')
+                        if lines and lines[0]:
+                            used, free = lines[0].split(', ')
+                            vram_gb = float(used) / 1024
+                            vram_free_gb = float(free) / 1024
+                except Exception:
+                    pass
+                
+                # Tracemalloc current usage
+                try:
+                    current, peak = tracemalloc.get_traced_memory()
+                    traced_mb = current / (1024**2)
+                except Exception:
+                    traced_mb = 0
+                
+                data_point = {
+                    'timestamp': time.time(),
+                    'rss_gb': rss_gb,
+                    'vram_gb': vram_gb,
+                    'vram_free_gb': vram_free_gb,
+                    'sys_used_gb': sys_used_gb,
+                    'sys_available_gb': sys_available_gb,
+                    'traced_mb': traced_mb
+                }
+                
+                self.data.append(data_point)
+                
+                # Print periodic updates
+                if len(self.data) % 10 == 0:  # Every 10 samples
+                    print(f"🔍 Memory: RSS={rss_gb:.2f}GB, VRAM={vram_gb:.2f}GB, Sys={sys_used_gb:.1f}GB")
+                    
+            except Exception as e:
+                print(f"Monitoring error: {e}")
+                
+            time.sleep(interval)
+    
+    def log_checkpoint(self, checkpoint_name: str):
+        """Log a specific checkpoint"""
+        if self.data:
+            self.data[-1]['checkpoint'] = checkpoint_name
+            latest = self.data[-1]
+            print(f"📍 CHECKPOINT [{checkpoint_name}]: RSS={latest['rss_gb']:.2f}GB, VRAM={latest['vram_gb']:.2f}GB")
+
+def run_with_profiling(config_path: str):
+    """Run the VR180 matting with memory profiling"""
+    profiler = MemoryProfiler("memory_profile_detailed.json")
+    
+    try:
+        # Start monitoring
+        profiler.start_monitoring(interval=2.0)  # Sample every 2 seconds
+        
+        # Log initial state
+        profiler.log_checkpoint("STARTUP")
+        
+        # Import after starting profiler to catch import memory usage
+        print("Importing VR180 processor...")
+        from vr180_matting.vr180_processor import VR180Processor
+        from vr180_matting.config import VR180Config
+        
+        profiler.log_checkpoint("IMPORTS_COMPLETE")
+        
+        # Load config
+        print(f"Loading config from {config_path}")
+        config = VR180Config.from_yaml(config_path)
+        
+        profiler.log_checkpoint("CONFIG_LOADED")
+        
+        # Initialize processor
+        print("Initializing VR180 processor...")
+        processor = VR180Processor(config)
+        
+        profiler.log_checkpoint("PROCESSOR_INITIALIZED")
+        
+        # Force garbage collection
+        gc.collect()
+        profiler.log_checkpoint("INITIAL_GC_COMPLETE")
+        
+        # Run processing
+        print("Starting VR180 processing...")
+        processor.process_video()
+        
+        profiler.log_checkpoint("PROCESSING_COMPLETE")
+        
+    except Exception as e:
+        print(f"❌ Error during processing: {e}")
+        profiler.log_checkpoint(f"ERROR: {str(e)}")
+        raise
+    finally:
+        # Stop monitoring and save results
+        profiler.stop_monitoring()
+        
+        # Print summary
+        print("\n" + "="*60)
+        print("MEMORY PROFILING SUMMARY")
+        print("="*60)
+        
+        if profiler.data:
+            peak_rss = max([d['rss_gb'] for d in profiler.data])
+            peak_vram = max([d['vram_gb'] for d in profiler.data])
+            
+            print(f"Peak RSS Memory: {peak_rss:.2f} GB")
+            print(f"Peak VRAM Usage: {peak_vram:.2f} GB")
+            print(f"Total Samples: {len(profiler.data)}")
+            
+            # Show checkpoints
+            checkpoints = [d for d in profiler.data if 'checkpoint' in d]
+            if checkpoints:
+                print(f"\nCheckpoints ({len(checkpoints)}):")
+                for cp in checkpoints:
+                    print(f"  {cp['checkpoint']}: RSS={cp['rss_gb']:.2f}GB, VRAM={cp['vram_gb']:.2f}GB")
+        
+        print(f"\nDetailed profile saved to: {profiler.output_file}")
+
+def main():
+    if len(sys.argv) != 2:
+        print("Usage: python memory_profiler_script.py <config.yaml>")
+        print("\nThis script runs VR180 matting with detailed memory profiling")
+        print("It will:")
+        print("- Monitor RSS, VRAM, and system memory every 2 seconds")
+        print("- Track memory allocations with tracemalloc")
+        print("- Log checkpoints at key processing stages")
+        print("- Save detailed JSON report for analysis")
+        sys.exit(1)
+    
+    config_path = sys.argv[1]
+    
+    if not Path(config_path).exists():
+        print(f"❌ Config file not found: {config_path}")
+        sys.exit(1)
+    
+    print("🚀 Starting VR180 Memory Profiling")
+    print(f"Config: {config_path}")
+    print("="*60)
+    
+    run_with_profiling(config_path)
+
+if __name__ == "__main__":
+    main()

vr180_matting/video_processor.py

@@ -387,19 +387,83 @@ class VideoProcessor:
             # Green screen background
             return np.full_like(frame, self.config.output.background_color, dtype=np.uint8)
     
+    def merge_chunks_streaming(self, chunk_files: List[Path], output_path: str, 
+                             overlap_frames: int = 0, audio_source: str = None) -> None:
+        """
+        Merge processed chunks using streaming approach (no memory accumulation)
+        
+        Args:
+            chunk_files: List of chunk result files (.npz)
+            output_path: Final output video path
+            overlap_frames: Number of overlapping frames
+            audio_source: Audio source file for final video
+        """
+        from .streaming_video_writer import StreamingVideoWriter
+        
+        if not chunk_files:
+            raise ValueError("No chunk files to merge")
+            
+        print(f"🎬 Streaming merge: {len(chunk_files)} chunks → {output_path}")
+        
+        # Initialize streaming writer
+        writer = StreamingVideoWriter(
+            output_path=output_path,
+            fps=self.video_info['fps'],
+            audio_source=audio_source
+        )
+        
+        try:
+            # Process each chunk without accumulation
+            for i, chunk_file in enumerate(chunk_files):
+                print(f"📼 Processing chunk {i+1}/{len(chunk_files)}: {chunk_file.name}")
+                
+                # Load chunk (this is the only copy in memory)
+                chunk_data = np.load(str(chunk_file))
+                frames = chunk_data['frames'].tolist()  # Convert to list of arrays
+                chunk_data.close()
+                
+                # Write chunk with streaming writer
+                writer.write_chunk(
+                    frames=frames,
+                    chunk_index=i,
+                    overlap_frames=overlap_frames if i > 0 else 0,
+                    blend_with_previous=(i > 0 and overlap_frames > 0)
+                )
+                
+                # Immediately free memory
+                del frames, chunk_data
+                
+                # Delete chunk file to free disk space
+                try:
+                    chunk_file.unlink()
+                    print(f"   🗑️  Deleted {chunk_file.name}")
+                except Exception as e:
+                    print(f"   ⚠️  Could not delete {chunk_file.name}: {e}")
+                
+                # Aggressive cleanup every chunk
+                self._aggressive_memory_cleanup(f"After processing chunk {i}")
+                
+            # Finalize the video
+            writer.finalize()
+            
+        except Exception as e:
+            print(f"❌ Streaming merge failed: {e}")
+            writer.cleanup()
+            raise
+            
+        print(f"✅ Streaming merge complete: {output_path}")
+    
     def merge_overlapping_chunks(self, 
                                 chunk_results: List[List[np.ndarray]], 
                                 overlap_frames: int) -> List[np.ndarray]:
         """
-        Merge overlapping chunks with blending in overlap regions
+        Legacy merge method - DEPRECATED due to memory accumulation
-        
+        Use merge_chunks_streaming() instead for memory efficiency
-        Args:
-            chunk_results: List of chunk results
-            overlap_frames: Number of overlapping frames
-            
-        Returns:
-            Merged frame sequence
         """
+        import warnings
+        warnings.warn("merge_overlapping_chunks() is deprecated due to memory accumulation. Use merge_chunks_streaming()", 
+                     DeprecationWarning, stacklevel=2)
+        
         if len(chunk_results) == 1:
             return chunk_results[0]
         
@@ -640,36 +704,23 @@ class VideoProcessor:
                 if self.memory_manager.should_emergency_cleanup():
                     self.memory_manager.emergency_cleanup()
             
-            # Load and merge chunks from disk
+            # Use streaming merge to avoid memory accumulation (fixes OOM)
-            print("\nLoading and merging chunks...")
+            print("\n🎬 Using streaming merge (no memory accumulation)...")
-            chunk_results = []
-            for i, chunk_file in enumerate(chunk_files):
-                print(f"Loading {chunk_file.name}...")
-                chunk_data = np.load(str(chunk_file))
-                chunk_results.append(chunk_data['frames'])
-                chunk_data.close()  # Close the file
             
-                # Delete chunk file immediately after loading to free disk space
+            # Determine audio source for final video
-                try:
+            audio_source = None
-                    chunk_file.unlink()
+            if self.config.output.preserve_audio and Path(self.config.input.video_path).exists():
-                    print(f"   Deleted chunk file {chunk_file.name}")
+                audio_source = self.config.input.video_path
-                except Exception as e:
-                    print(f"   Warning: Could not delete chunk file: {e}")
             
-                # Aggressive cleanup every few chunks to prevent accumulation
+            # Stream merge chunks directly to output (no memory accumulation)
-                if i % 3 == 0 and i > 0:
+            self.merge_chunks_streaming(
-                    self._aggressive_memory_cleanup(f"after loading chunk {i}")
+                chunk_files=chunk_files,
+                output_path=self.config.output.path,
+                overlap_frames=overlap_frames,
+                audio_source=audio_source
+            )
             
-            # Merge chunks
+            print("✅ Streaming merge complete - no memory accumulation!")
-            final_frames = self.merge_overlapping_chunks(chunk_results, overlap_frames)
-            
-            # Free chunk results after merging - this is critical!
-            del chunk_results
-            self._aggressive_memory_cleanup("after merging chunks")
-            
-            # Save results
-            print(f"Saving {len(final_frames)} processed frames...")
-            self.save_video(final_frames, self.config.output.path)
             
             # Calculate final statistics
             self.processing_stats['end_time'] = time.time()

vr180_matting/vr180_processor.py

@@ -398,44 +398,50 @@ class VR180Processor(VideoProcessor):
             
             self._print_memory_step(f"After SAM2 propagation ({eye_name} eye)")
             
-            # Apply masks - need to reload frames from temp video since we freed the original frames
+            # Apply masks with streaming approach (no frame accumulation)
-            self._print_memory_step(f"Before reloading frames for mask application ({eye_name} eye)")
+            self._print_memory_step(f"Before streaming mask application ({eye_name} eye)")
             
-            # Read frames back from the temp video for mask application
+            # Process frames one at a time without accumulation
             cap = cv2.VideoCapture(str(temp_video_path))
-            reloaded_frames = []
-            
-            for frame_idx in range(num_frames):
-                ret, frame = cap.read()
-                if not ret:
-                    break
-                reloaded_frames.append(frame)
-            cap.release()
-            
-            self._print_memory_step(f"Reloaded {len(reloaded_frames)} frames for mask application")
-            
-            # Apply masks
             matted_frames = []
-            for frame_idx, frame in enumerate(reloaded_frames):
-                if frame_idx in video_segments:
-                    frame_masks = video_segments[frame_idx]
-                    combined_mask = self.sam2_model.get_combined_mask(frame_masks)
             
-                    matted_frame = self.sam2_model.apply_mask_to_frame(
+            try:
-                        frame, combined_mask,
+                for frame_idx in range(num_frames):
-                        output_format=self.config.output.format,
+                    ret, frame = cap.read()
-                        background_color=self.config.output.background_color
+                    if not ret:
-                    )
+                        break
-                else:
-                    matted_frame = self._create_empty_mask_frame(frame)
                     
-                matted_frames.append(matted_frame)
+                    # Apply mask to this single frame
+                    if frame_idx in video_segments:
+                        frame_masks = video_segments[frame_idx]
+                        combined_mask = self.sam2_model.get_combined_mask(frame_masks)
                         
-            # Free reloaded frames and video segments completely
+                        matted_frame = self.sam2_model.apply_mask_to_frame(
-            del reloaded_frames
+                            frame, combined_mask,
+                            output_format=self.config.output.format,
+                            background_color=self.config.output.background_color
+                        )
+                    else:
+                        matted_frame = self._create_empty_mask_frame(frame)
+                    
+                    matted_frames.append(matted_frame)
+                    
+                    # Free the original frame immediately (no accumulation)
+                    del frame
+                    
+                    # Periodic cleanup during processing
+                    if frame_idx % 100 == 0 and frame_idx > 0:
+                        import gc
+                        gc.collect()
+                        
+            finally:
+                cap.release()
+            
+            # Free video segments completely
             del video_segments  # This holds processed masks from SAM2
-            self._aggressive_memory_cleanup(f"After mask application ({eye_name} eye)")
+            self._aggressive_memory_cleanup(f"After streaming mask application ({eye_name} eye)")
             
+            self._print_memory_step(f"Completed streaming mask application ({eye_name} eye)")
             return matted_frames
             
         finally: