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please god work

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Scott Register
2025-07-26 17:44:23 -07:00
parent 262cb00b69
commit 3a547b7c21
2 changed files with 26 additions and 9 deletions
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15
vr180_matting/checkpoint_manager.py
View File

@@ -140,12 +140,25 @@ class CheckpointManager:
def get_completed_chunk_files(self) -> List[Path]:
"""Get list of all completed chunk files in order"""
chunk_files = []
missing_chunks = []
for i in range(self.status['total_chunks']):
chunk_file = self.get_chunk_file(i)
if chunk_file:
chunk_files.append(chunk_file)
else:
break # Stop at first missing chunk
# Check if chunk is marked as completed but file is missing
if self.is_chunk_completed(i):
missing_chunks.append(i)
print(f"⚠️ Chunk {i} marked complete but file missing!")
else:
break # Stop at first unprocessed chunk
if missing_chunks:
print(f"❌ Missing checkpoint files for chunks: {missing_chunks}")
print(f" This may happen if files were deleted during streaming merge")
print(f" These chunks may need to be reprocessed")
return chunk_files
def mark_processing_complete(self):

20
vr180_matting/video_processor.py
View File

@@ -453,20 +453,17 @@ class VideoProcessor:
chunk_data.close()
del chunk_data, frames_array
# 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}")
# Don't delete checkpoint files - they're needed for potential resume
# The checkpoint system manages cleanup separately
print(f" 📋 Keeping checkpoint file: {chunk_file.name}")
# Aggressive cleanup and memory monitoring after each chunk
self._aggressive_memory_cleanup(f"After streaming merge chunk {i}")
# Memory safety check
memory_info = self._get_process_memory_info()
if memory_info['rss_gb'] > 35: # Warning if approaching 46GB limit
print(f"⚠️ High memory usage: {memory_info['rss_gb']:.1f}GB - forcing cleanup")
if memory_info['total_process_gb'] > 35: # Warning if approaching 46GB limit
print(f"⚠️ High memory usage: {memory_info['total_process_gb']:.1f}GB - forcing cleanup")
gc.collect()
import torch
if torch.cuda.is_available():
@@ -882,6 +879,13 @@ class VideoProcessor:
# Use streaming merge to avoid memory accumulation (fixes OOM)
print("\n🎬 Using streaming merge (no memory accumulation)...")
# For resume scenarios, make sure we have all chunk files
if resume_info['can_resume']:
checkpoint_chunk_files = checkpoint_mgr.get_completed_chunk_files()
if len(checkpoint_chunk_files) != len(chunk_files):
print(f"⚠️ Using {len(checkpoint_chunk_files)} checkpoint files instead of {len(chunk_files)} temp files")
chunk_files = checkpoint_chunk_files
# Determine audio source for final video
audio_source = None
if self.config.output.preserve_audio and Path(self.config.input.video_path).exists():