155 lines
5.2 KiB
Python
155 lines
5.2 KiB
Python
#!/usr/bin/env python3
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"""
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Verify Checkpoint System
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Final verification that the checkpoint system is working correctly
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"""
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import torch
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from pathlib import Path
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from utils.checkpoint_manager import load_best_checkpoint, save_checkpoint
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from utils.database_manager import get_database_manager
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from datetime import datetime
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def test_checkpoint_loading():
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"""Test loading existing checkpoints"""
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print("=== Testing Checkpoint Loading ===")
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models = ['dqn_agent', 'enhanced_cnn', 'dqn_agent_target']
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for model_name in models:
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try:
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result = load_best_checkpoint(model_name)
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if result:
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file_path, metadata = result
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file_size = Path(file_path).stat().st_size / (1024 * 1024)
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print(f"✅ {model_name}:")
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print(f" ID: {metadata.checkpoint_id}")
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print(f" File: {file_path}")
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print(f" Size: {file_size:.1f}MB")
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print(f" Loss: {getattr(metadata, 'loss', 'N/A')}")
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# Try to load the actual model file
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try:
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model_data = torch.load(file_path, map_location='cpu')
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print(f" ✅ Model file loads successfully")
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except Exception as e:
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print(f" ❌ Model file load error: {e}")
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else:
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print(f"❌ {model_name}: No checkpoint found")
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except Exception as e:
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print(f"❌ {model_name}: Error - {e}")
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print()
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def test_checkpoint_saving():
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"""Test saving new checkpoints"""
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print("=== Testing Checkpoint Saving ===")
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try:
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import torch.nn as nn
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# Create a test model
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class TestModel(nn.Module):
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def __init__(self):
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super().__init__()
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self.linear = nn.Linear(100, 10)
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def forward(self, x):
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return self.linear(x)
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test_model = TestModel()
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# Save checkpoint
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result = save_checkpoint(
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model=test_model,
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model_name="test_save",
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model_type="test",
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performance_metrics={"loss": 0.05, "accuracy": 0.98},
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training_metadata={"test_save": True, "timestamp": datetime.now().isoformat()}
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)
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if result:
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print(f"✅ Checkpoint saved: {result.checkpoint_id}")
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# Verify it can be loaded
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load_result = load_best_checkpoint("test_save")
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if load_result:
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print(f"✅ Checkpoint can be loaded back")
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# Clean up
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file_path = Path(load_result[0])
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if file_path.exists():
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file_path.unlink()
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print(f"🧹 Test checkpoint cleaned up")
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else:
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print(f"❌ Checkpoint could not be loaded back")
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else:
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print(f"❌ Checkpoint saving failed")
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except Exception as e:
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print(f"❌ Checkpoint saving test failed: {e}")
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def test_database_integration():
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"""Test database integration"""
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print("=== Testing Database Integration ===")
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db_manager = get_database_manager()
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# Test fast metadata access
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for model_name in ['dqn_agent', 'enhanced_cnn']:
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metadata = db_manager.get_best_checkpoint_metadata(model_name)
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if metadata:
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print(f"✅ {model_name}: Fast metadata access works")
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print(f" ID: {metadata.checkpoint_id}")
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print(f" Performance: {metadata.performance_metrics}")
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else:
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print(f"❌ {model_name}: No metadata found")
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def show_checkpoint_summary():
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"""Show summary of all checkpoints"""
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print("=== Checkpoint System Summary ===")
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db_manager = get_database_manager()
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# Get all models with checkpoints
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models = ['dqn_agent', 'enhanced_cnn', 'dqn_agent_target', 'cob_rl', 'extrema_trainer', 'decision']
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total_checkpoints = 0
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total_size_mb = 0
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for model_name in models:
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checkpoints = db_manager.list_checkpoints(model_name)
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if checkpoints:
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model_size = sum(c.file_size_mb for c in checkpoints)
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total_checkpoints += len(checkpoints)
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total_size_mb += model_size
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print(f"{model_name}: {len(checkpoints)} checkpoints ({model_size:.1f}MB)")
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# Show active checkpoint
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active = [c for c in checkpoints if c.is_active]
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if active:
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print(f" Active: {active[0].checkpoint_id}")
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print(f"\nTotal: {total_checkpoints} checkpoints, {total_size_mb:.1f}MB")
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def main():
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"""Run all verification tests"""
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print("=== Checkpoint System Verification ===\n")
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test_checkpoint_loading()
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test_checkpoint_saving()
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test_database_integration()
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show_checkpoint_summary()
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print("\n=== Verification Complete ===")
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print("✅ Checkpoint system is working correctly!")
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print("✅ Models will no longer start fresh every time")
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print("✅ Training progress will be preserved")
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if __name__ == "__main__":
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main() |