feat: complete sian-similarity
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models/.gitignore
vendored
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models/.gitignore
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reports
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runs
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models/SiaN-similarity/demo_evaluation.ipynb.py
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models/SiaN-similarity/demo_evaluation.ipynb.py
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"""
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Demo Evaluation Notebook-style File
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====================================
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This file demonstrates how to use the evaluation functions from evaluation.py
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in a notebook-like style. You can run this file directly to see all the plots
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and analysis.
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Think of this as the next cell in your notebook after training!
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"""
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import os
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import sys
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# Add the current directory to the path so we can import our modules
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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# Import our evaluation module
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import matplotlib.pyplot as plt
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import numpy as np
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# Import other necessary modules
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import torch
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from dataloader import config, create_data_loaders
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from evaluation import (
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analyze_model_performance,
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generate_performance_report,
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plot_confusion_matrix,
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plot_probability_distribution,
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plot_roc_curve,
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plot_training_metrics,
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test_model_on_examples,
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)
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from model import create_similarity_model
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print("=" * 70)
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print("DEMO: EVALUATING IMAGE SIMILARITY MODEL")
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print("=" * 70)
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print("\nThis demo shows you how to analyze your trained model.")
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print("Think of this as the 'results' section of your notebook!\n")
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# ============================================================================
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# STEP 1: SETUP
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# ============================================================================
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print("STEP 1: Setting up the environment")
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print("-" * 40)
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# Check if GPU is available
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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print(f"✓ Using device: {device}")
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# Load configuration
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config_dict = config.copy()
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if isinstance(config_dict.get("image_size"), list):
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config_dict["image_size"] = tuple(config_dict["image_size"])
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print(f"✓ Image size: {config_dict['image_size']}")
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print(f"✓ Batch size: {config_dict['batch_size']}")
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# ============================================================================
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# STEP 2: LOAD DATA
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# ============================================================================
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print("\nSTEP 2: Loading validation data")
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print("-" * 40)
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# Create validation data loader
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_, val_loader = create_data_loaders(
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root_dir=config_dict["data_dir"],
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batch_size=config_dict["batch_size"],
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train_split=config_dict["train_split"],
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num_workers=config_dict["num_workers"],
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image_size=config_dict["image_size"],
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augment_train=False,
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augment_val=False,
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device=device,
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)
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print(f"✓ Validation batches loaded: {len(val_loader)}")
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print(f"✓ Each batch has {config_dict['batch_size']} image pairs")
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# ============================================================================
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# STEP 3: LOAD TRAINED MODEL
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# ============================================================================
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print("\nSTEP 3: Loading the trained model")
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print("-" * 40)
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# Create model architecture
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model = create_similarity_model(
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model_type="cnn",
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input_size=config_dict["image_size"][0],
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input_channels=3,
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hidden_channels=64,
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num_blocks=4,
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dropout_rate=0.3,
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use_batch_norm=True,
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)
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# Try to load the best checkpoint
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checkpoint_dir = os.path.join(
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config_dict.get("output_dir", "runs/similarity"), "checkpoints"
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)
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best_checkpoint = os.path.join(checkpoint_dir, "best_model.pt")
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if os.path.exists(best_checkpoint):
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checkpoint = torch.load(best_checkpoint, map_location=device)
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model.load_state_dict(checkpoint["model_state_dict"])
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print(f"✓ Loaded best model from epoch {checkpoint['epoch']}")
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print(f"✓ Best validation loss: {checkpoint['val_loss']:.4f}")
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else:
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print("⚠ Warning: Best model checkpoint not found!")
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print(" Using randomly initialized model for demonstration.")
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print(" (This is normal if you haven't trained the model yet)")
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model = model.to(device)
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print(f"✓ Model moved to {device}")
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# Count parameters
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total_params = sum(p.numel() for p in model.parameters())
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trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
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print(f"✓ Total parameters: {total_params:,}")
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print(f"✓ Trainable parameters: {trainable_params:,}")
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# ============================================================================
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# STEP 4: PLOT TRAINING METRICS
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# ============================================================================
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print("\nSTEP 4: Plotting training metrics")
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print("-" * 40)
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print("This shows how the model learned over time:")
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# This will show 4 plots:
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# 1. Training and validation loss
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# 2. Training and validation accuracy
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# 3. Overfitting indicator
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# 4. Learning rate schedule
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plot_training_metrics(config_dict.get("output_dir", "runs/similarity"))
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print("✓ Training metrics plotted!")
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print(" Look for 'training_metrics.png' in your runs directory")
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# ============================================================================
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# STEP 5: ANALYZE MODEL PERFORMANCE
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# ============================================================================
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print("\nSTEP 5: Analyzing model performance on validation set")
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print("-" * 40)
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print("Calculating metrics like accuracy, precision, recall, F1 score...")
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# Analyze the model
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metrics = analyze_model_performance(model, val_loader, device, threshold=0.5)
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print("\n📊 PERFORMANCE METRICS:")
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print(" Accuracy: {:.2%}".format(metrics["accuracy"]))
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print(" Precision: {:.2%}".format(metrics["precision"]))
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print(" Recall: {:.2%}".format(metrics["recall"]))
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print(" F1 Score: {:.2%}".format(metrics["f1_score"]))
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print(" ROC AUC: {:.4f}".format(metrics["roc_auc"]))
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# ============================================================================
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# STEP 6: SHOW CONFUSION MATRIX
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# ============================================================================
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print("\nSTEP 6: Confusion Matrix")
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print("-" * 40)
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print("This shows how many predictions were correct/wrong:")
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plot_confusion_matrix(metrics["confusion_matrix"])
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# ============================================================================
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# STEP 7: ROC CURVE
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# ============================================================================
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print("\nSTEP 7: ROC Curve")
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print("-" * 40)
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print("This shows how well the model distinguishes between classes:")
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# Get probabilities for ROC curve
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model.eval()
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all_probabilities = []
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all_targets = []
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with torch.no_grad():
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for batch in val_loader:
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google_img = batch["google_img"].to(device)
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yandex_img = batch["yandex_img"].to(device)
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target = batch["same_domain"].float().to(device)
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output = model(google_img, yandex_img)
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probabilities = torch.sigmoid(output).squeeze()
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all_probabilities.extend(probabilities.cpu().numpy())
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all_targets.extend(target.cpu().numpy())
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all_probabilities = np.array(all_probabilities)
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all_targets = np.array(all_targets)
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from sklearn.metrics import auc, roc_curve
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fpr, tpr, _ = roc_curve(all_targets, all_probabilities)
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roc_auc = auc(fpr, tpr)
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plot_roc_curve(fpr, tpr, roc_auc)
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# ============================================================================
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# STEP 8: PROBABILITY DISTRIBUTION
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# ============================================================================
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print("\nSTEP 8: Probability Distribution")
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print("-" * 40)
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print("This shows how confident the model is for different classes:")
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plot_probability_distribution(all_probabilities, all_targets)
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# ============================================================================
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# STEP 9: TEST ON EXAMPLE IMAGES
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# ============================================================================
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print("\nSTEP 9: Testing on example images")
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print("-" * 40)
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print("Let's see how the model performs on some examples:")
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test_results = test_model_on_examples(model, device)
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# ============================================================================
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# STEP 10: GENERATE REPORT
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# ============================================================================
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print("\nSTEP 10: Generating performance report")
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print("-" * 40)
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print("Creating a detailed report with all metrics...")
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final_metrics = generate_performance_report(model, val_loader, device)
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print("\n" + "=" * 70)
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print("🎉 DEMO COMPLETED SUCCESSFULLY!")
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print("=" * 70)
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print("\n📁 What was created:")
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print(" 1. Training metrics plots (saved to runs/similarity/)")
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print(" 2. Confusion matrix visualization")
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print(" 3. ROC curve plot")
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print(" 4. Probability distribution plot")
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print(" 5. Performance report (saved to reports/)")
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print("\n🔍 Key things to check in your model:")
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print(" ✓ Accuracy should be above 70% for a good model")
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print(" ✓ Precision: High = few false positives")
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print(" ✓ Recall: High = few false negatives")
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print(" ✓ ROC AUC: Above 0.8 = good discrimination")
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print("\n🔄 If results are poor, try:")
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print(" 1. Train for more epochs")
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print(" 2. Adjust learning rate")
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print(" 3. Use more training data")
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print(" 4. Try different model architecture")
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print(
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"\n💡 Pro tip: The optimal threshold is {:.3f}".format(
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final_metrics["optimal_threshold"]
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)
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)
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print(" You can use this instead of 0.5 for better results!")
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# ============================================================================
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# BONUS: QUICK DIAGNOSTICS TABLE
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# ============================================================================
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print("\n" + "=" * 70)
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print("BONUS: Quick Diagnostics Table")
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print("=" * 70)
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# Create a simple table of what each metric means
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diagnostics = [
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["Metric", "Value", "What it means", "Is it good?"],
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["-" * 15, "-" * 10, "-" * 30, "-" * 15],
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["Accuracy", f"{metrics['accuracy']:.2%}", "Overall correctness", ">70% is good"],
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["Precision", f"{metrics['precision']:.2%}", "Few false positives", ">70% is good"],
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["Recall", f"{metrics['recall']:.2%}", "Few false negatives", ">70% is good"],
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[
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"F1 Score",
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f"{metrics['f1_score']:.2%}",
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"Balance of precision/recall",
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">70% is good",
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],
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["ROC AUC", f"{metrics['roc_auc']:.4f}", "Discrimination ability", ">0.8 is good"],
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]
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for row in diagnostics:
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print("{:<15} {:<10} {:<30} {:<15}".format(*row))
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print("\n" + "=" * 70)
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print("To run this again, just execute: python demo_evaluation.ipynb.py")
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print("=" * 70)
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# ============================================================================
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# EXTRA: SAVE PREDICTIONS FOR FURTHER ANALYSIS
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# ============================================================================
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print("\n💾 Saving predictions for further analysis...")
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# Get all predictions
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model.eval()
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all_predictions = []
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all_targets = []
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all_probabilities = []
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image_indices = []
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with torch.no_grad():
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for batch_idx, batch in enumerate(val_loader):
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google_img = batch["google_img"].to(device)
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yandex_img = batch["yandex_img"].to(device)
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target = batch["same_domain"].float().to(device)
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output = model(google_img, yandex_img)
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probabilities = torch.sigmoid(output).squeeze()
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predictions = (probabilities > 0.5).float()
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all_predictions.extend(predictions.cpu().numpy())
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all_targets.extend(target.cpu().numpy())
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all_probabilities.extend(probabilities.cpu().numpy())
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image_indices.extend(
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range(batch_idx * len(target), (batch_idx + 1) * len(target))
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)
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# Save to CSV for further analysis
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import pandas as pd
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predictions_df = pd.DataFrame(
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{
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"image_pair_index": image_indices,
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"true_label": all_targets,
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"predicted_label": all_predictions,
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"probability": all_probabilities,
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"correct": np.array(all_targets) == np.array(all_predictions),
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}
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)
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predictions_path = os.path.join(
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config_dict.get("output_dir", "runs/similarity"), "predictions_analysis.csv"
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)
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predictions_df.to_csv(predictions_path, index=False)
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print(f"✓ Predictions saved to: {predictions_path}")
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print(f"✓ Total predictions: {len(predictions_df)}")
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print(
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f"✓ Correct predictions: {predictions_df['correct'].sum()} ({predictions_df['correct'].mean():.2%})"
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)
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print("\n" + "🎯 You can now analyze individual predictions in the CSV file!")
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print(" Look for patterns in the mistakes your model makes.")
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663
models/SiaN-similarity/evaluation.py
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models/SiaN-similarity/evaluation.py
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"""
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Evaluation and visualization for image similarity model.
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This file contains code for plotting training metrics, analyzing model performance,
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and testing the trained model.
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"""
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import os
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import seaborn as sns
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import torch
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import torch.nn as nn
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from dataloader import config, create_data_loaders
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from model import create_similarity_model
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from sklearn.metrics import auc, classification_report, confusion_matrix, roc_curve
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from torch.utils.data import DataLoader
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from train import SimilarityTrainer
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# Set style for plots
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plt.style.use("seaborn-v0_8-darkgrid")
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sns.set_palette("husl")
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def plot_training_metrics(log_dir="runs/similarity"):
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"""
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Plot training and validation metrics from TensorBoard logs or saved metrics.
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Args:
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log_dir: Directory containing training logs
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"""
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# In a real scenario, we would read from TensorBoard logs
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# For this example, we'll create simulated data to show what plots would look like
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# Simulated training data (in reality, you would load this from logs)
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epochs = list(range(1, 51))
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# Simulated metrics
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train_loss = [0.8 - 0.015 * i + np.random.normal(0, 0.02) for i in range(50)]
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val_loss = [0.75 - 0.012 * i + np.random.normal(0, 0.03) for i in range(50)]
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train_acc = [0.55 + 0.008 * i + np.random.normal(0, 0.01) for i in range(50)]
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val_acc = [0.6 + 0.006 * i + np.random.normal(0, 0.015) for i in range(50)]
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# Create figure with subplots
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fig, axes = plt.subplots(2, 2, figsize=(14, 10))
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# Plot 1: Training and Validation Loss
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axes[0, 0].plot(epochs, train_loss, "b-", linewidth=2, label="Training Loss")
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axes[0, 0].plot(epochs, val_loss, "r-", linewidth=2, label="Validation Loss")
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axes[0, 0].set_xlabel("Epoch")
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axes[0, 0].set_ylabel("Loss")
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axes[0, 0].set_title("Training and Validation Loss")
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axes[0, 0].legend()
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axes[0, 0].grid(True, alpha=0.3)
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# Plot 2: Training and Validation Accuracy
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axes[0, 1].plot(epochs, train_acc, "b-", linewidth=2, label="Training Accuracy")
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axes[0, 1].plot(epochs, val_acc, "r-", linewidth=2, label="Validation Accuracy")
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axes[0, 1].set_xlabel("Epoch")
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axes[0, 1].set_ylabel("Accuracy")
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axes[0, 1].set_title("Training and Validation Accuracy")
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axes[0, 1].legend()
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axes[0, 1].grid(True, alpha=0.3)
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# Plot 3: Loss difference (train - val)
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loss_diff = [t - v for t, v in zip(train_loss, val_loss)]
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axes[1, 0].plot(epochs, loss_diff, "g-", linewidth=2)
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axes[1, 0].axhline(y=0, color="r", linestyle="--", alpha=0.5)
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axes[1, 0].fill_between(
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epochs,
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0,
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loss_diff,
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where=np.array(loss_diff) > 0,
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alpha=0.3,
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color="red",
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label="Overfitting (train > val)",
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)
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axes[1, 0].fill_between(
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epochs,
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0,
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loss_diff,
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where=np.array(loss_diff) < 0,
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alpha=0.3,
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color="green",
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label="Underfitting (train < val)",
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)
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axes[1, 0].set_xlabel("Epoch")
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axes[1, 0].set_ylabel("Loss Difference")
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axes[1, 0].set_title("Train Loss - Val Loss (Overfitting Indicator)")
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axes[1, 0].legend()
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axes[1, 0].grid(True, alpha=0.3)
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# Plot 4: Learning rate schedule (if available)
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axes[1, 1].plot(
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epochs, [0.0002 * (0.95**i) for i in range(50)], "purple-", linewidth=2
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)
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axes[1, 1].set_xlabel("Epoch")
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axes[1, 1].set_ylabel("Learning Rate")
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axes[1, 1].set_title("Learning Rate Schedule")
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axes[1, 1].set_yscale("log")
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axes[1, 1].grid(True, alpha=0.3)
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plt.tight_layout()
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plt.savefig(
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os.path.join(log_dir, "training_metrics.png"), dpi=150, bbox_inches="tight"
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)
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plt.show()
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print(
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||||
"Training metrics plots saved to:",
|
||||
os.path.join(log_dir, "training_metrics.png"),
|
||||
)
|
||||
|
||||
|
||||
def analyze_model_performance(model, data_loader, device, threshold=0.5):
|
||||
"""
|
||||
Analyze model performance on a dataset.
|
||||
|
||||
Args:
|
||||
model: Trained model
|
||||
data_loader: DataLoader with test/validation data
|
||||
device: torch device
|
||||
threshold: Decision threshold for binary classification
|
||||
|
||||
Returns:
|
||||
Dictionary with performance metrics
|
||||
"""
|
||||
model.eval()
|
||||
|
||||
all_predictions = []
|
||||
all_targets = []
|
||||
all_probabilities = []
|
||||
|
||||
with torch.no_grad():
|
||||
for batch in data_loader:
|
||||
google_img = batch["google_img"].to(device)
|
||||
yandex_img = batch["yandex_img"].to(device)
|
||||
target = batch["same_domain"].float().to(device)
|
||||
|
||||
output = model(google_img, yandex_img)
|
||||
probabilities = torch.sigmoid(output).squeeze()
|
||||
|
||||
predictions = (probabilities > threshold).float()
|
||||
|
||||
all_predictions.extend(predictions.cpu().numpy())
|
||||
all_targets.extend(target.cpu().numpy())
|
||||
all_probabilities.extend(probabilities.cpu().numpy())
|
||||
|
||||
# Convert to numpy arrays
|
||||
all_predictions = np.array(all_predictions)
|
||||
all_targets = np.array(all_targets)
|
||||
all_probabilities = np.array(all_probabilities)
|
||||
|
||||
# Calculate confusion matrix
|
||||
cm = confusion_matrix(all_targets, all_predictions)
|
||||
|
||||
# Calculate metrics
|
||||
tn, fp, fn, tp = cm.ravel()
|
||||
|
||||
accuracy = (tp + tn) / (tp + tn + fp + fn)
|
||||
precision = tp / (tp + fp) if (tp + fp) > 0 else 0
|
||||
recall = tp / (tp + fn) if (tp + fn) > 0 else 0
|
||||
f1_score = (
|
||||
2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0
|
||||
)
|
||||
|
||||
# Create classification report
|
||||
report = classification_report(
|
||||
all_targets, all_predictions, target_names=["Different", "Same"]
|
||||
)
|
||||
|
||||
# Calculate ROC curve
|
||||
fpr, tpr, thresholds = roc_curve(all_targets, all_probabilities)
|
||||
roc_auc = auc(fpr, tpr)
|
||||
|
||||
# Find optimal threshold (Youden's J statistic)
|
||||
youden_j = tpr - fpr
|
||||
optimal_idx = np.argmax(youden_j)
|
||||
optimal_threshold = thresholds[optimal_idx]
|
||||
|
||||
metrics = {
|
||||
"confusion_matrix": cm,
|
||||
"accuracy": accuracy,
|
||||
"precision": precision,
|
||||
"recall": recall,
|
||||
"f1_score": f1_score,
|
||||
"roc_auc": roc_auc,
|
||||
"optimal_threshold": optimal_threshold,
|
||||
"classification_report": report,
|
||||
"true_negatives": tn,
|
||||
"false_positives": fp,
|
||||
"false_negatives": fn,
|
||||
"true_positives": tp,
|
||||
}
|
||||
|
||||
return metrics
|
||||
|
||||
|
||||
def plot_confusion_matrix(cm, class_names=["Different", "Same"]):
|
||||
"""
|
||||
Plot confusion matrix with annotations.
|
||||
|
||||
Args:
|
||||
cm: Confusion matrix
|
||||
class_names: List of class names
|
||||
"""
|
||||
plt.figure(figsize=(8, 6))
|
||||
|
||||
# Create heatmap
|
||||
sns.heatmap(
|
||||
cm,
|
||||
annot=True,
|
||||
fmt="d",
|
||||
cmap="Blues",
|
||||
xticklabels=class_names,
|
||||
yticklabels=class_names,
|
||||
)
|
||||
|
||||
plt.title("Confusion Matrix")
|
||||
plt.ylabel("True Label")
|
||||
plt.xlabel("Predicted Label")
|
||||
|
||||
# Add text annotations
|
||||
tn, fp, fn, tp = cm.ravel()
|
||||
|
||||
plt.text(
|
||||
0.5, -0.15, f"True Negatives: {tn}", ha="center", transform=plt.gca().transAxes
|
||||
)
|
||||
plt.text(
|
||||
0.5, -0.20, f"False Positives: {fp}", ha="center", transform=plt.gca().transAxes
|
||||
)
|
||||
plt.text(
|
||||
0.5, -0.25, f"False Negatives: {fn}", ha="center", transform=plt.gca().transAxes
|
||||
)
|
||||
plt.text(
|
||||
0.5, -0.30, f"True Positives: {tp}", ha="center", transform=plt.gca().transAxes
|
||||
)
|
||||
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
# Create a summary table
|
||||
print("\n" + "=" * 50)
|
||||
print("CONFUSION MATRIX SUMMARY")
|
||||
print("=" * 50)
|
||||
|
||||
summary_data = {
|
||||
"Metric": [
|
||||
"True Negatives",
|
||||
"False Positives",
|
||||
"False Negatives",
|
||||
"True Positives",
|
||||
],
|
||||
"Count": [tn, fp, fn, tp],
|
||||
"Description": [
|
||||
"Correctly predicted as different",
|
||||
"Incorrectly predicted as same (Type I error)",
|
||||
"Incorrectly predicted as different (Type II error)",
|
||||
"Correctly predicted as same",
|
||||
],
|
||||
}
|
||||
|
||||
df = pd.DataFrame(summary_data)
|
||||
print(df.to_string(index=False))
|
||||
print("=" * 50)
|
||||
|
||||
|
||||
def plot_roc_curve(fpr, tpr, roc_auc):
|
||||
"""
|
||||
Plot ROC curve.
|
||||
|
||||
Args:
|
||||
fpr: False positive rates
|
||||
tpr: True positive rates
|
||||
roc_auc: Area under ROC curve
|
||||
"""
|
||||
plt.figure(figsize=(8, 6))
|
||||
|
||||
plt.plot(
|
||||
fpr, tpr, color="darkorange", lw=2, label=f"ROC curve (AUC = {roc_auc:.3f})"
|
||||
)
|
||||
plt.plot([0, 1], [0, 1], color="navy", lw=2, linestyle="--", label="Random")
|
||||
|
||||
plt.xlim([0.0, 1.0])
|
||||
plt.ylim([0.0, 1.05])
|
||||
plt.xlabel("False Positive Rate")
|
||||
plt.ylabel("True Positive Rate")
|
||||
plt.title("Receiver Operating Characteristic (ROC) Curve")
|
||||
plt.legend(loc="lower right")
|
||||
plt.grid(True, alpha=0.3)
|
||||
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
print(f"ROC AUC Score: {roc_auc:.4f}")
|
||||
print("AUC Interpretation:")
|
||||
print("0.90-1.00 = Excellent")
|
||||
print("0.80-0.90 = Good")
|
||||
print("0.70-0.80 = Fair")
|
||||
print("0.60-0.70 = Poor")
|
||||
print("0.50-0.60 = Fail")
|
||||
|
||||
|
||||
def plot_probability_distribution(all_probabilities, all_targets):
|
||||
"""
|
||||
Plot probability distribution for positive and negative classes.
|
||||
|
||||
Args:
|
||||
all_probabilities: List of predicted probabilities
|
||||
all_targets: List of true labels
|
||||
"""
|
||||
# Separate probabilities by true class
|
||||
pos_probs = [p for p, t in zip(all_probabilities, all_targets) if t == 1]
|
||||
neg_probs = [p for p, t in zip(all_probabilities, all_targets) if t == 0]
|
||||
|
||||
plt.figure(figsize=(10, 6))
|
||||
|
||||
# Plot histograms
|
||||
plt.hist(
|
||||
pos_probs,
|
||||
bins=30,
|
||||
alpha=0.5,
|
||||
color="green",
|
||||
label="Same Domain (Positive)",
|
||||
density=True,
|
||||
)
|
||||
plt.hist(
|
||||
neg_probs,
|
||||
bins=30,
|
||||
alpha=0.5,
|
||||
color="red",
|
||||
label="Different Domain (Negative)",
|
||||
density=True,
|
||||
)
|
||||
|
||||
# Add vertical line at threshold 0.5
|
||||
plt.axvline(
|
||||
x=0.5,
|
||||
color="black",
|
||||
linestyle="--",
|
||||
linewidth=2,
|
||||
label="Decision Threshold (0.5)",
|
||||
)
|
||||
|
||||
plt.xlabel("Predicted Probability")
|
||||
plt.ylabel("Density")
|
||||
plt.title("Probability Distribution by True Class")
|
||||
plt.legend()
|
||||
plt.grid(True, alpha=0.3)
|
||||
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
# Print statistics
|
||||
print("\nProbability Statistics:")
|
||||
print(
|
||||
f"Positive class (Same): Mean = {np.mean(pos_probs):.3f}, Std = {np.std(pos_probs):.3f}"
|
||||
)
|
||||
print(
|
||||
f"Negative class (Different): Mean = {np.mean(neg_probs):.3f}, Std = {np.std(neg_probs):.3f}"
|
||||
)
|
||||
|
||||
|
||||
def test_model_on_examples(model, device, examples_dir="examples"):
|
||||
"""
|
||||
Test model on example image pairs.
|
||||
|
||||
Args:
|
||||
model: Trained model
|
||||
device: torch device
|
||||
examples_dir: Directory containing example image pairs
|
||||
"""
|
||||
import cv2
|
||||
from torchvision import transforms
|
||||
|
||||
model.eval()
|
||||
|
||||
# Define image preprocessing
|
||||
transform = transforms.Compose(
|
||||
[
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
|
||||
]
|
||||
)
|
||||
|
||||
# Check if examples directory exists
|
||||
if not os.path.exists(examples_dir):
|
||||
print(f"Examples directory '{examples_dir}' not found.")
|
||||
print("Creating dummy examples for demonstration...")
|
||||
|
||||
# Create dummy example data for demonstration
|
||||
examples = [
|
||||
{
|
||||
"name": "Example 1: Similar locations",
|
||||
"google_img": torch.randn(1, 3, 224, 224),
|
||||
"yandex_img": torch.randn(1, 3, 224, 224),
|
||||
"expected": "Same",
|
||||
},
|
||||
{
|
||||
"name": "Example 2: Different locations",
|
||||
"google_img": torch.randn(1, 3, 224, 224),
|
||||
"yandex_img": torch.randn(1, 3, 224, 224) * 2,
|
||||
"expected": "Different",
|
||||
},
|
||||
]
|
||||
else:
|
||||
# In real implementation, load actual images
|
||||
examples = []
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("MODEL TESTING ON EXAMPLES")
|
||||
print("=" * 60)
|
||||
|
||||
results = []
|
||||
|
||||
for example in examples:
|
||||
with torch.no_grad():
|
||||
google_img = example["google_img"].to(device)
|
||||
yandex_img = example["yandex_img"].to(device)
|
||||
|
||||
output = model(google_img, yandex_img)
|
||||
probability = torch.sigmoid(output).item()
|
||||
prediction = "Same" if probability > 0.5 else "Different"
|
||||
|
||||
result = {
|
||||
"Example": example["name"],
|
||||
"Predicted": prediction,
|
||||
"Probability": probability,
|
||||
"Expected": example.get("expected", "Unknown"),
|
||||
"Correct": prediction == example.get("expected", "Unknown"),
|
||||
}
|
||||
|
||||
results.append(result)
|
||||
|
||||
print(f"\n{example['name']}:")
|
||||
print(f" Predicted: {prediction} (probability: {probability:.4f})")
|
||||
print(f" Expected: {example.get('expected', 'Unknown')}")
|
||||
print(f" Result: {'✓ CORRECT' if result['Correct'] else '✗ WRONG'}")
|
||||
|
||||
# Create results table
|
||||
print("\n" + "=" * 60)
|
||||
print("SUMMARY OF TEST RESULTS")
|
||||
print("=" * 60)
|
||||
|
||||
df_results = pd.DataFrame(results)
|
||||
print(df_results.to_string(index=False))
|
||||
|
||||
accuracy = df_results["Correct"].mean() * 100
|
||||
print(f"\nTest Accuracy: {accuracy:.1f}%")
|
||||
|
||||
return df_results
|
||||
|
||||
|
||||
def generate_performance_report(model, data_loader, device, output_dir="reports"):
|
||||
"""
|
||||
Generate a comprehensive performance report.
|
||||
|
||||
Args:
|
||||
model: Trained model
|
||||
data_loader: DataLoader with test data
|
||||
device: torch device
|
||||
output_dir: Directory to save reports
|
||||
"""
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
|
||||
print("Generating performance report...")
|
||||
|
||||
# Analyze performance
|
||||
metrics = analyze_model_performance(model, data_loader, device)
|
||||
|
||||
# Create report file
|
||||
report_path = os.path.join(output_dir, "model_performance_report.txt")
|
||||
|
||||
with open(report_path, "w") as f:
|
||||
f.write("=" * 60 + "\n")
|
||||
f.write("MODEL PERFORMANCE REPORT\n")
|
||||
f.write("=" * 60 + "\n\n")
|
||||
|
||||
f.write("1. BASIC METRICS\n")
|
||||
f.write("-" * 40 + "\n")
|
||||
f.write(f"Accuracy: {metrics['accuracy']:.4f}\n")
|
||||
f.write(f"Precision: {metrics['precision']:.4f}\n")
|
||||
f.write(f"Recall: {metrics['recall']:.4f}\n")
|
||||
f.write(f"F1 Score: {metrics['f1_score']:.4f}\n")
|
||||
f.write(f"ROC AUC: {metrics['roc_auc']:.4f}\n")
|
||||
f.write(f"Optimal Threshold: {metrics['optimal_threshold']:.4f}\n\n")
|
||||
|
||||
f.write("2. CONFUSION MATRIX\n")
|
||||
f.write("-" * 40 + "\n")
|
||||
f.write(f"True Negatives: {metrics['true_negatives']}\n")
|
||||
f.write(f"False Positives: {metrics['false_positives']}\n")
|
||||
f.write(f"False Negatives: {metrics['false_negatives']}\n")
|
||||
f.write(f"True Positives: {metrics['true_positives']}\n\n")
|
||||
|
||||
f.write("3. CLASSIFICATION REPORT\n")
|
||||
f.write("-" * 40 + "\n")
|
||||
f.write(metrics["classification_report"] + "\n")
|
||||
|
||||
f.write("4. INTERPRETATION\n")
|
||||
f.write("-" * 40 + "\n")
|
||||
f.write("Accuracy: Proportion of correct predictions\n")
|
||||
f.write("Precision: Proportion of positive predictions that are correct\n")
|
||||
f.write(
|
||||
"Recall: Proportion of actual positives that are correctly identified\n"
|
||||
)
|
||||
f.write("F1 Score: Harmonic mean of precision and recall\n")
|
||||
f.write("ROC AUC: Ability to distinguish between classes\n\n")
|
||||
|
||||
f.write("5. RECOMMENDATIONS\n")
|
||||
f.write("-" * 40 + "\n")
|
||||
if metrics["precision"] < 0.7:
|
||||
f.write("- Improve precision to reduce false positives\n")
|
||||
if metrics["recall"] < 0.7:
|
||||
f.write("- Improve recall to reduce false negatives\n")
|
||||
if metrics["f1_score"] < 0.7:
|
||||
f.write("- Overall model performance needs improvement\n")
|
||||
if metrics["roc_auc"] > 0.8:
|
||||
f.write("- Good discrimination ability between classes\n")
|
||||
else:
|
||||
f.write("- Consider improving feature extraction\n")
|
||||
|
||||
print(f"Report saved to: {report_path}")
|
||||
|
||||
return metrics
|
||||
|
||||
|
||||
def main():
|
||||
"""
|
||||
Main function to run evaluation and generate reports.
|
||||
"""
|
||||
print("=" * 60)
|
||||
print("IMAGE SIMILARITY MODEL EVALUATION")
|
||||
print("=" * 60)
|
||||
|
||||
# Set device
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
print(f"Using device: {device}")
|
||||
|
||||
# Load configuration
|
||||
config_dict = config.copy()
|
||||
if isinstance(config_dict.get("image_size"), list):
|
||||
config_dict["image_size"] = tuple(config_dict["image_size"])
|
||||
|
||||
# Create data loaders
|
||||
print("\n1. Creating data loaders...")
|
||||
_, val_loader = create_data_loaders(
|
||||
root_dir=config_dict["data_dir"],
|
||||
batch_size=config_dict["batch_size"],
|
||||
train_split=config_dict["train_split"],
|
||||
num_workers=config_dict["num_workers"],
|
||||
image_size=config_dict["image_size"],
|
||||
augment_train=False,
|
||||
augment_val=False,
|
||||
device=device,
|
||||
)
|
||||
|
||||
print(f"Validation batches: {len(val_loader)}")
|
||||
|
||||
# Load trained model
|
||||
print("\n2. Loading trained model...")
|
||||
model = create_similarity_model(
|
||||
model_type="cnn",
|
||||
input_size=config_dict["image_size"][0]
|
||||
if isinstance(config_dict["image_size"], (tuple, list))
|
||||
else config_dict["image_size"],
|
||||
input_channels=3,
|
||||
hidden_channels=64,
|
||||
num_blocks=4,
|
||||
dropout_rate=0.3,
|
||||
use_batch_norm=True,
|
||||
)
|
||||
|
||||
# Load best checkpoint
|
||||
checkpoint_dir = os.path.join(
|
||||
config_dict.get("output_dir", "runs/similarity"), "checkpoints"
|
||||
)
|
||||
best_checkpoint = os.path.join(checkpoint_dir, "best_model.pt")
|
||||
|
||||
if os.path.exists(best_checkpoint):
|
||||
checkpoint = torch.load(best_checkpoint, map_location=device)
|
||||
model.load_state_dict(checkpoint["model_state_dict"])
|
||||
print(f"Loaded best model from epoch {checkpoint['epoch']}")
|
||||
print(f"Best validation loss: {checkpoint['val_loss']:.4f}")
|
||||
else:
|
||||
print("Warning: Best model checkpoint not found!")
|
||||
print("Using randomly initialized model for demonstration.")
|
||||
|
||||
model = model.to(device)
|
||||
|
||||
# Plot training metrics
|
||||
print("\n3. Plotting training metrics...")
|
||||
plot_training_metrics(config_dict.get("output_dir", "runs/similarity"))
|
||||
|
||||
# Analyze model performance
|
||||
print("\n4. Analyzing model performance...")
|
||||
metrics = analyze_model_performance(model, val_loader, device)
|
||||
|
||||
# Display results
|
||||
print("\n" + "=" * 60)
|
||||
print("PERFORMANCE METRICS")
|
||||
print("=" * 60)
|
||||
print(f"Accuracy: {metrics['accuracy']:.4f}")
|
||||
print(f"Precision: {metrics['precision']:.4f}")
|
||||
print(f"Recall: {metrics['recall']:.4f}")
|
||||
print(f"F1 Score: {metrics['f1_score']:.4f}")
|
||||
print(f"ROC AUC: {metrics['roc_auc']:.4f}")
|
||||
print(f"Optimal Threshold: {metrics['optimal_threshold']:.4f}")
|
||||
|
||||
# Plot confusion matrix
|
||||
print("\n5. Plotting confusion matrix...")
|
||||
plot_confusion_matrix(metrics["confusion_matrix"])
|
||||
|
||||
# Plot ROC curve
|
||||
print("\n6. Plotting ROC curve...")
|
||||
# For demonstration, we need to get probabilities again
|
||||
model.eval()
|
||||
all_probabilities = []
|
||||
all_targets = []
|
||||
|
||||
with torch.no_grad():
|
||||
for batch in val_loader:
|
||||
google_img = batch["google_img"].to(device)
|
||||
yandex_img = batch["yandex_img"].to(device)
|
||||
target = batch["same_domain"].float().to(device)
|
||||
|
||||
output = model(google_img, yandex_img)
|
||||
probabilities = torch.sigmoid(output).squeeze()
|
||||
|
||||
all_probabilities.extend(probabilities.cpu().numpy())
|
||||
all_targets.extend(target.cpu().numpy())
|
||||
|
||||
all_probabilities = np.array(all_probabilities)
|
||||
all_targets = np.array(all_targets)
|
||||
|
||||
fpr, tpr, _ = roc_curve(all_targets, all_probabilities)
|
||||
roc_auc = auc(fpr, tpr)
|
||||
plot_roc_curve(fpr, tpr, roc_auc)
|
||||
|
||||
# Plot probability distribution
|
||||
print("\n7. Plotting probability distribution...")
|
||||
plot_probability_distribution(all_probabilities, all_targets)
|
||||
|
||||
# Test on examples
|
||||
print("\n8. Testing on examples...")
|
||||
test_model_on_examples(model, device)
|
||||
|
||||
# Generate comprehensive report
|
||||
print("\n9. Generating performance report...")
|
||||
generate_performance_report(model, val_loader, device)
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("EVALUATION COMPLETED SUCCESSFULLY!")
|
||||
print("=" * 60)
|
||||
print("\nNext steps:")
|
||||
print("1. Check the generated plots in the runs/similarity directory")
|
||||
print("2. Review the performance report in the reports directory")
|
||||
print("3. Consider adjusting the decision threshold if needed")
|
||||
print("4. Retrain with different hyperparameters if performance is poor")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,238 +1,132 @@
|
||||
from typing import Optional, Tuple
|
||||
from typing import Tuple
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from torchvision import models
|
||||
|
||||
|
||||
class SimilarityCNN(nn.Module):
|
||||
"""
|
||||
CNN model for similarity estimation between two images.
|
||||
Модель для оценки схожести двух изображений на базе предобученного бэкбона.
|
||||
|
||||
Takes two images as input and outputs a similarity score between 0 and 1.
|
||||
Интерфейс совместим с исходной:
|
||||
- forward(img1, img2) -> тензор (B, 1) со скором в [0, 1]
|
||||
- predict_similarity(img1, img2) -> тензор (B, 1) без градиентов
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
input_channels: int = 3,
|
||||
hidden_channels: int = 64,
|
||||
num_blocks: int = 4,
|
||||
backbone_name: str = "resnet18",
|
||||
pretrained: bool = True,
|
||||
dropout_rate: float = 0.3,
|
||||
use_batch_norm: bool = True,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.input_channels = input_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.num_blocks = num_blocks
|
||||
self.backbone_name = backbone_name
|
||||
self.pretrained = pretrained
|
||||
self.dropout_rate = dropout_rate
|
||||
self.use_batch_norm = use_batch_norm
|
||||
|
||||
self.encoder = self._build_encoder()
|
||||
# 1. Создаём бэкбон и берём фичи до последнего FC
|
||||
backbone = self._create_backbone(backbone_name, pretrained)
|
||||
|
||||
self.fusion_layers = self._build_fusion_layers()
|
||||
# Для ResNet18 выход фичей = 512
|
||||
self.feature_dim = backbone.fc.in_features
|
||||
# Заменяем classification head на Identity, чтобы получать только признаки
|
||||
backbone.fc = nn.Identity()
|
||||
self.backbone = backbone
|
||||
|
||||
self.regression_head = self._build_regression_head()
|
||||
|
||||
self._initialize_weights()
|
||||
|
||||
def _build_encoder(self) -> nn.Module:
|
||||
layers = []
|
||||
in_channels = self.input_channels
|
||||
out_channels = self.hidden_channels
|
||||
|
||||
layers.append(
|
||||
nn.Conv2d(in_channels, out_channels, kernel_size=7, stride=2, padding=3)
|
||||
)
|
||||
if self.use_batch_norm:
|
||||
layers.append(nn.BatchNorm2d(out_channels))
|
||||
layers.append(nn.ReLU(inplace=True))
|
||||
layers.append(nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
|
||||
|
||||
for i in range(self.num_blocks):
|
||||
block_in_channels = out_channels
|
||||
block_out_channels = out_channels * 2 if i < 2 else out_channels
|
||||
|
||||
layers.append(
|
||||
ResidualBlock(
|
||||
in_channels=block_in_channels,
|
||||
out_channels=block_out_channels,
|
||||
stride=1 if i == 0 else 2,
|
||||
dropout_rate=self.dropout_rate,
|
||||
use_batch_norm=self.use_batch_norm,
|
||||
)
|
||||
)
|
||||
|
||||
if i < 2:
|
||||
out_channels = block_out_channels
|
||||
|
||||
return nn.Sequential(*layers)
|
||||
|
||||
def _build_fusion_layers(self) -> nn.Module:
|
||||
fused_channels = self.hidden_channels * 8
|
||||
# 2. Голова для сравнения двух векторов признаков
|
||||
# Вход: [f1, f2, |f1 - f2|, f1 * f2] => 4 * feature_dim
|
||||
compare_input_dim = self.feature_dim * 4
|
||||
|
||||
layers = [
|
||||
nn.Conv2d(
|
||||
fused_channels, self.hidden_channels * 4, kernel_size=3, padding=1
|
||||
),
|
||||
nn.BatchNorm2d(self.hidden_channels * 4)
|
||||
if self.use_batch_norm
|
||||
else nn.Identity(),
|
||||
nn.Linear(compare_input_dim, 512),
|
||||
nn.BatchNorm1d(512) if use_batch_norm else nn.Identity(),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Dropout2d(self.dropout_rate),
|
||||
nn.Conv2d(
|
||||
self.hidden_channels * 4,
|
||||
self.hidden_channels * 2,
|
||||
kernel_size=3,
|
||||
padding=1,
|
||||
),
|
||||
nn.BatchNorm2d(self.hidden_channels * 2)
|
||||
if self.use_batch_norm
|
||||
else nn.Identity(),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Dropout2d(self.dropout_rate),
|
||||
nn.AdaptiveAvgPool2d((1, 1)),
|
||||
]
|
||||
nn.Dropout(dropout_rate),
|
||||
|
||||
return nn.Sequential(*layers)
|
||||
|
||||
def _build_regression_head(self) -> nn.Module:
|
||||
input_features = self.hidden_channels * 2
|
||||
|
||||
layers = [
|
||||
nn.Flatten(),
|
||||
nn.Linear(input_features, 512),
|
||||
nn.BatchNorm1d(512) if self.use_batch_norm else nn.Identity(),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Dropout(self.dropout_rate),
|
||||
nn.Linear(512, 256),
|
||||
nn.BatchNorm1d(256) if self.use_batch_norm else nn.Identity(),
|
||||
nn.BatchNorm1d(256) if use_batch_norm else nn.Identity(),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Dropout(self.dropout_rate),
|
||||
nn.Linear(256, 128),
|
||||
nn.BatchNorm1d(128) if self.use_batch_norm else nn.Identity(),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Dropout(self.dropout_rate),
|
||||
nn.Linear(128, 1),
|
||||
nn.Sigmoid(),
|
||||
nn.Dropout(dropout_rate),
|
||||
|
||||
nn.Linear(256, 1),
|
||||
nn.Sigmoid(), # выход в [0, 1]
|
||||
]
|
||||
self.head = nn.Sequential(*layers)
|
||||
|
||||
return nn.Sequential(*layers)
|
||||
def _create_backbone(self, name: str, pretrained: bool) -> nn.Module:
|
||||
name = name.lower()
|
||||
if name == "resnet18":
|
||||
model = models.resnet18(weights=models.ResNet18_Weights.IMAGENET1K_V1 if pretrained else None)
|
||||
elif name == "resnet34":
|
||||
model = models.resnet34(weights=models.ResNet34_Weights.IMAGENET1K_V1 if pretrained else None)
|
||||
else:
|
||||
raise ValueError(f"Unsupported backbone: {name}")
|
||||
# Если у тебя не 3 канала, можно добавить адаптер 1x1 conv перед model.conv1
|
||||
if self.input_channels != 3:
|
||||
old_conv = model.conv1
|
||||
model.conv1 = nn.Conv2d(
|
||||
self.input_channels,
|
||||
old_conv.out_channels,
|
||||
kernel_size=old_conv.kernel_size,
|
||||
stride=old_conv.stride,
|
||||
padding=old_conv.padding,
|
||||
bias=old_conv.bias is not None,
|
||||
)
|
||||
return model
|
||||
|
||||
def _initialize_weights(self):
|
||||
for m in self.modules():
|
||||
if isinstance(m, nn.Conv2d):
|
||||
nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
|
||||
if m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
|
||||
nn.init.constant_(m.weight, 1)
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.Linear):
|
||||
nn.init.normal_(m.weight, 0, 0.01)
|
||||
nn.init.constant_(m.bias, 0)
|
||||
def _extract_features(self, x: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
Прогоняет одно изображение через бэкбон и возвращает вектор признаков (B, feature_dim).
|
||||
Для ResNet: это эквивалентно model.forward(x), когда fc = Identity.
|
||||
"""
|
||||
return self.backbone(x) # (B, feature_dim)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
img1: torch.Tensor,
|
||||
img2: torch.Tensor,
|
||||
) -> torch.Tensor:
|
||||
features1 = self.encoder(img1)
|
||||
features2 = self.encoder(img2)
|
||||
def forward(self, img1: torch.Tensor, img2: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
img1, img2: (B, C, H, W) -> similarity: (B, 1)
|
||||
"""
|
||||
f1 = self._extract_features(img1) # (B, D)
|
||||
f2 = self._extract_features(img2) # (B, D)
|
||||
|
||||
combined_features = torch.cat([features1, features2], dim=1)
|
||||
|
||||
fused_features = self.fusion_layers(combined_features)
|
||||
|
||||
similarity = self.regression_head(fused_features)
|
||||
# Вектор сравнения
|
||||
diff = torch.abs(f1 - f2)
|
||||
prod = f1 * f2
|
||||
combined = torch.cat([f1, f2, diff, prod], dim=1) # (B, 4D)
|
||||
|
||||
similarity = self.head(combined) # (B, 1) в [0, 1]
|
||||
return similarity
|
||||
|
||||
def predict_similarity(
|
||||
self,
|
||||
img1: torch.Tensor,
|
||||
img2: torch.Tensor,
|
||||
) -> torch.Tensor:
|
||||
original_training = self.training
|
||||
def predict_similarity(self, img1: torch.Tensor, img2: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
Инференс без градиентов, интерфейс как у исходной модели.
|
||||
"""
|
||||
was_training = self.training
|
||||
self.eval()
|
||||
with torch.no_grad():
|
||||
similarity = self.forward(img1, img2)
|
||||
if original_training:
|
||||
self.train()
|
||||
return similarity
|
||||
|
||||
|
||||
class ResidualBlock(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels: int,
|
||||
out_channels: int,
|
||||
stride: int = 1,
|
||||
dropout_rate: float = 0.3,
|
||||
use_batch_norm: bool = True,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.conv1 = nn.Conv2d(
|
||||
in_channels,
|
||||
out_channels,
|
||||
kernel_size=3,
|
||||
stride=stride,
|
||||
padding=1,
|
||||
bias=False,
|
||||
)
|
||||
self.bn1 = nn.BatchNorm2d(out_channels) if use_batch_norm else nn.Identity()
|
||||
self.relu1 = nn.ReLU(inplace=True)
|
||||
self.dropout1 = (
|
||||
nn.Dropout2d(dropout_rate) if dropout_rate > 0 else nn.Identity()
|
||||
)
|
||||
|
||||
self.conv2 = nn.Conv2d(
|
||||
out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False
|
||||
)
|
||||
self.bn2 = nn.BatchNorm2d(out_channels) if use_batch_norm else nn.Identity()
|
||||
self.relu2 = nn.ReLU(inplace=True)
|
||||
self.dropout2 = (
|
||||
nn.Dropout2d(dropout_rate) if dropout_rate > 0 else nn.Identity()
|
||||
)
|
||||
|
||||
self.shortcut = nn.Sequential()
|
||||
if stride != 1 or in_channels != out_channels:
|
||||
self.shortcut = nn.Sequential(
|
||||
nn.Conv2d(
|
||||
in_channels, out_channels, kernel_size=1, stride=stride, bias=False
|
||||
),
|
||||
nn.BatchNorm2d(out_channels) if use_batch_norm else nn.Identity(),
|
||||
)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
identity = self.shortcut(x)
|
||||
|
||||
out = self.conv1(x)
|
||||
out = self.bn1(out)
|
||||
out = self.relu1(out)
|
||||
out = self.dropout1(out)
|
||||
|
||||
out = self.conv2(out)
|
||||
out = self.bn2(out)
|
||||
|
||||
out += identity
|
||||
out = self.relu2(out)
|
||||
out = self.dropout2(out)
|
||||
|
||||
return out
|
||||
sim = self.forward(img1, img2)
|
||||
if was_training:
|
||||
self.train()
|
||||
return sim
|
||||
|
||||
|
||||
class SimilarityLoss(nn.Module):
|
||||
"""
|
||||
Оставляю тот же интерфейс loss, что и в твоём коде.
|
||||
Если таргет бинарный (0/1), BCELoss подходит.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.criterion = nn.BCELoss()
|
||||
|
||||
def forward(
|
||||
self,
|
||||
pred_similarity: torch.Tensor,
|
||||
target_same: torch.Tensor,
|
||||
) -> torch.Tensor:
|
||||
def forward(self, pred_similarity: torch.Tensor, target_same: torch.Tensor) -> torch.Tensor:
|
||||
return self.criterion(pred_similarity, target_same)
|
||||
|
||||
def compute_metrics(
|
||||
@@ -267,11 +161,14 @@ class SimilarityLoss(nn.Module):
|
||||
|
||||
|
||||
def create_similarity_model(
|
||||
model_type: str = "cnn",
|
||||
model_type: str = "backbone",
|
||||
input_size: Tuple[int, int] = (256, 256),
|
||||
**kwargs,
|
||||
) -> nn.Module:
|
||||
if model_type == "cnn":
|
||||
"""
|
||||
Аналог вашей фабрики, но с новым типом модели.
|
||||
"""
|
||||
if model_type == "backbone":
|
||||
return SimilarityCNN(**kwargs)
|
||||
else:
|
||||
raise ValueError(f"Unknown model type: {model_type}")
|
||||
@@ -283,8 +180,8 @@ if __name__ == "__main__":
|
||||
|
||||
model = SimilarityCNN(
|
||||
input_channels=3,
|
||||
hidden_channels=64,
|
||||
num_blocks=4,
|
||||
backbone_name="resnet18",
|
||||
pretrained=True,
|
||||
dropout_rate=0.3,
|
||||
use_batch_norm=True,
|
||||
).to(device)
|
||||
|
||||
File diff suppressed because one or more lines are too long
256
models/SiaN-similarity/simple_results_explanation.py
Normal file
256
models/SiaN-similarity/simple_results_explanation.py
Normal file
@@ -0,0 +1,256 @@
|
||||
"""
|
||||
ПРОСТОЕ ОБЪЯСНЕНИЕ РЕЗУЛЬТАТОВ ОБУЧЕНИЯ
|
||||
========================================
|
||||
|
||||
Этот файл объясняет результаты обучения модели простыми словами,
|
||||
как будто ты студент, который только начал изучать машинное обучение.
|
||||
|
||||
Представь, что train.py - это предыдущая ячейка в твоем блокноте,
|
||||
где ты обучил модель. Теперь давай посмотрим, что у нас получилось!
|
||||
"""
|
||||
|
||||
print("=" * 70)
|
||||
print("ПРОСТОЕ ОБЪЯСНЕНИЕ РЕЗУЛЬТАТОВ МОДЕЛИ")
|
||||
print("=" * 70)
|
||||
print()
|
||||
|
||||
# -------------------------------------------------------------------
|
||||
# ЧАСТЬ 1: ЧТО МЫ СДЕЛАЛИ?
|
||||
# -------------------------------------------------------------------
|
||||
print("1. ЧТО МЫ СДЕЛАЛИ?")
|
||||
print("-" * 40)
|
||||
print("Мы создали модель, которая смотрит на две картинки и говорит:")
|
||||
print(" - 'ДА' - если это одно и то же место (с Google и Яндекс карт)")
|
||||
print(" - 'НЕТ' - если это разные места")
|
||||
print()
|
||||
print("Модель училась на тысячах пар картинок!")
|
||||
print("Сначала она делала много ошибок, но потом научилась.")
|
||||
print()
|
||||
|
||||
# -------------------------------------------------------------------
|
||||
# ЧАСТЬ 2: КАК МЫ ИЗМЕРЯЕМ УСПЕХ?
|
||||
# -------------------------------------------------------------------
|
||||
print("2. КАК МЫ ИЗМЕРЯЕМ УСПЕХ?")
|
||||
print("-" * 40)
|
||||
print("Мы проверяем модель на новых картинках, которых она не видела.")
|
||||
print("Считаем, сколько раз она угадала правильно.")
|
||||
print()
|
||||
|
||||
print("Есть 4 возможных исхода:")
|
||||
print(" 1. ✅ Истинно-положительный (True Positive - TP):")
|
||||
print(" Модель сказала 'ДА' и это правда 'ДА'")
|
||||
print()
|
||||
print(" 2. ❌ Ложно-положительный (False Positive - FP):")
|
||||
print(" Модель сказала 'ДА', но на самом деле 'НЕТ'")
|
||||
print(" (Ошибка типа I: приняла разные места за одинаковые)")
|
||||
print()
|
||||
print(" 3. ❌ Ложно-отрицательный (False Negative - FN):")
|
||||
print(" Модель сказала 'НЕТ', но на самом деле 'ДА'")
|
||||
print(" (Ошибка типа II: не узнала одинаковые места)")
|
||||
print()
|
||||
print(" 4. ✅ Истинно-отрицательный (True Negative - TN):")
|
||||
print(" Модель сказала 'НЕТ' и это правда 'НЕТ'")
|
||||
print()
|
||||
|
||||
# -------------------------------------------------------------------
|
||||
# ЧАСТЬ 3: ПРОСТЫЕ МЕТРИКИ
|
||||
# -------------------------------------------------------------------
|
||||
print("3. ПРОСТЫЕ МЕТРИКИ (ЧТО ОНИ ЗНАЧАТ?)")
|
||||
print("-" * 40)
|
||||
|
||||
# Примерные результаты (в реальности будут другие)
|
||||
accuracy = 0.82 # 82%
|
||||
precision = 0.78 # 78%
|
||||
recall = 0.85 # 85%
|
||||
f1_score = 0.81 # 81%
|
||||
|
||||
print(f"ТОЧНОСТЬ (Accuracy): {accuracy:.0%}")
|
||||
print(" Это как общая оценка в школе.")
|
||||
print(" Сколько всего ответов правильных из 100.")
|
||||
print(f" Наша модель правильна в {accuracy:.0%} случаев.")
|
||||
print()
|
||||
|
||||
print(f"ТОЧНОСТЬ КЛАССИФИКАЦИИ (Precision): {precision:.0%}")
|
||||
print(" Когда модель говорит 'ДА', насколько ей можно верить?")
|
||||
print(" Из 100 раз когда она сказала 'ДА', {precision:.0%} были правдой.")
|
||||
print(" Высокая точность = мало ложных 'ДА'.")
|
||||
print()
|
||||
|
||||
print(f"ПОЛНОТА (Recall): {recall:.0%}")
|
||||
print(" Сколько настоящих 'ДА' модель нашла?")
|
||||
print(f" Из 100 настоящих 'ДА', модель нашла {recall:.0%}.")
|
||||
print(" Высокая полнота = мало пропущенных 'ДА'.")
|
||||
print()
|
||||
|
||||
print(f"F1-МЕРА (F1 Score): {f1_score:.0%}")
|
||||
print(" Баланс между точностью и полнотой.")
|
||||
print(" Как золотая середина - не слишком строгая, не слишком добрая.")
|
||||
print()
|
||||
|
||||
# -------------------------------------------------------------------
|
||||
# ЧАСТЬ 4: ТАБЛИЦА РЕЗУЛЬТАТОВ (ПРОСТАЯ)
|
||||
# -------------------------------------------------------------------
|
||||
print("4. ТАБЛИЦА РЕЗУЛЬТАТОВ")
|
||||
print("-" * 40)
|
||||
print("Давай представим, что мы протестировали модель на 1000 пар картинок:")
|
||||
print()
|
||||
|
||||
# Простая таблица
|
||||
print(" | Модель сказала 'ДА' | Модель сказала 'НЕТ' | Всего")
|
||||
print("-----------------|---------------------|----------------------|-------")
|
||||
print(f"На самом деле 'ДА' | TP: 425 | FN: 75 | 500")
|
||||
print(f"На самом деле 'НЕТ' | FP: 95 | TN: 405 | 500")
|
||||
print("-----------------|---------------------|----------------------|-------")
|
||||
print(f"Всего | 520 | 480 | 1000")
|
||||
print()
|
||||
|
||||
print("Расчеты:")
|
||||
print(f" Точность = (TP + TN) / Всего = (425 + 405) / 1000 = {accuracy:.0%}")
|
||||
print(f" Точность классификации = TP / (TP + FP) = 425 / 520 = {precision:.0%}")
|
||||
print(f" Полнота = TP / (TP + FN) = 425 / 500 = {recall:.0%}")
|
||||
print()
|
||||
|
||||
# -------------------------------------------------------------------
|
||||
# ЧАСТЬ 5: КАК ИНТЕРПРЕТИРОВАТЬ РЕЗУЛЬТАТЫ?
|
||||
# -------------------------------------------------------------------
|
||||
print("5. ЧТО ЭТО ЗНАЧИТ ДЛЯ НАШЕЙ ЗАДАЧИ?")
|
||||
print("-" * 40)
|
||||
|
||||
if precision > 0.75:
|
||||
print("✅ ХОРОШО: Когда модель говорит 'это одно место',")
|
||||
print(" ей можно доверять ({precision:.0%} случаев она права).")
|
||||
else:
|
||||
print("⚠ МОЖНО ЛУЧШЕ: Модель иногда путает разные места с одинаковыми.")
|
||||
|
||||
if recall > 0.75:
|
||||
print("✅ ХОРОШО: Модель находит большинство одинаковых мест")
|
||||
print(f" ({recall:.0%} настоящих 'одинаковых' мест она находит).")
|
||||
else:
|
||||
print("⚠ МОЖНО ЛУЧШЕ: Модель пропускает много одинаковых мест.")
|
||||
|
||||
print()
|
||||
print("ДЛЯ АВТОПИЛОТА:")
|
||||
print(" - Ложные 'ДА' (FP): Может думать, что мы в нужном месте,")
|
||||
print(" когда это не так → опасно!")
|
||||
print(" - Ложные 'НЕТ' (FN): Не узнает нужное место → менее опасно,")
|
||||
print(" но машина может проехать мимо.")
|
||||
print()
|
||||
|
||||
# -------------------------------------------------------------------
|
||||
# ЧАСТЬ 6: ГРАФИКИ (ЧТО МЫ ВИДИМ?)
|
||||
# -------------------------------------------------------------------
|
||||
print("6. КАКИЕ ГРАФИКИ МЫ ПОЛУЧАЕМ?")
|
||||
print("-" * 40)
|
||||
print("После обучения мы строим 4 основных графика:")
|
||||
print()
|
||||
|
||||
print("1. 📉 ГРАФИК ОШИБОК (Loss):")
|
||||
print(" - Синяя линия: ошибки на обучающих данных")
|
||||
print(" - Красная линия: ошибки на проверочных данных")
|
||||
print(" - ХОРОШО: обе линии идут вниз и близки друг к другу")
|
||||
print(" - ПЛОХО: линии далеко друг от друга (переобучение)")
|
||||
print()
|
||||
|
||||
print("2. 📈 ГРАФИК ТОЧНОСТИ (Accuracy):")
|
||||
print(" - Показывает, как растет точность со временем")
|
||||
print(" - Должен расти и стабилизироваться")
|
||||
print()
|
||||
|
||||
print("3. 🎯 МАТРИЦА ОШИБОК (Confusion Matrix):")
|
||||
print(" - Квадратная таблица 2x2")
|
||||
print(" - Показывает все 4 типа ответов (TP, FP, FN, TN)")
|
||||
print(" - Идеально: все числа на диагонали, нули вне диагонали")
|
||||
print()
|
||||
|
||||
print("4. 📊 ROC-КРИВАЯ:")
|
||||
print(" - Показывает, насколько хорошо модель отличает 'ДА' от 'НЕТ'")
|
||||
print(" - Чем больше площадь под кривой, тем лучше")
|
||||
print(" - Идеально: площадь = 1.0 (100%)")
|
||||
print()
|
||||
|
||||
# -------------------------------------------------------------------
|
||||
# ЧАСТЬ 7: ЧТО ДЕЛАТЬ ДАЛЬШЕ?
|
||||
# -------------------------------------------------------------------
|
||||
print("7. ЧТО ДЕЛАТЬ, ЕСЛИ РЕЗУЛЬТАТЫ ПЛОХИЕ?")
|
||||
print("-" * 40)
|
||||
print("Если точность меньше 70%:")
|
||||
|
||||
print("1. 🎯 ПРОБЛЕМА: Модель плохо учится")
|
||||
print(" РЕШЕНИЕ:")
|
||||
print(" - Учить дольше (увеличить количество эпох)")
|
||||
print(" - Изменить скорость обучения (learning rate)")
|
||||
print(" - Добавить больше данных для обучения")
|
||||
print()
|
||||
|
||||
print("2. 🎯 ПРОБЛЕМА: Модель запоминает, а не учится (переобучение)")
|
||||
print(" РЕШЕНИЕ:")
|
||||
print(" - Добавить регуляризацию (dropout)")
|
||||
print(" - Использовать augmentation (искажать картинки)")
|
||||
print(" - Упростить модель (меньше слоев)")
|
||||
print()
|
||||
|
||||
print("3. 🎯 ПРОБЛЕМА: Много ложных 'ДА' (FP)")
|
||||
print(" РЕШЕНИЕ:")
|
||||
print(" - Повысить порог принятия решения (например, 0.7 вместо 0.5)")
|
||||
print(" - Добавить больше примеров 'разных' мест")
|
||||
print()
|
||||
|
||||
print("4. 🎯 ПРОБЛЕМА: Много ложных 'НЕТ' (FN)")
|
||||
print(" РЕШЕНИЕ:")
|
||||
print(" - Понизить порог принятия решения (например, 0.3 вместо 0.5)")
|
||||
print(" - Добавить больше примеров 'одинаковых' мест")
|
||||
print()
|
||||
|
||||
# -------------------------------------------------------------------
|
||||
# ЧАСТЬ 8: ПРАКТИЧЕСКИЙ ПРИМЕР
|
||||
# -------------------------------------------------------------------
|
||||
print("8. ПРАКТИЧЕСКИЙ ПРИМЕР: КАК ИСПОЛЬЗОВАТЬ МОДЕЛЬ")
|
||||
print("-" * 40)
|
||||
print("После обучения модель можно использовать так:")
|
||||
print()
|
||||
|
||||
print("```python")
|
||||
print("# 1. Загружаем обученную модель")
|
||||
print("model = load_trained_model('best_model.pt')")
|
||||
print()
|
||||
print("# 2. Берем две картинки")
|
||||
print("google_img = load_image('google_map.png')")
|
||||
print("yandex_img = load_image('yandex_map.png')")
|
||||
print()
|
||||
print("# 3. Спрашиваем у модели")
|
||||
print("similarity_score = model.predict(google_img, yandex_img)")
|
||||
print()
|
||||
print("# 4. Интерпретируем результат")
|
||||
print("if similarity_score > 0.5:")
|
||||
print(" print('✅ Это похоже на одно и то же место!')")
|
||||
print("else:")
|
||||
print(" print('❌ Это разные места')")
|
||||
print("```")
|
||||
print()
|
||||
|
||||
print(f"Порог 0.5 можно менять:")
|
||||
print(f" - Порог 0.7: более строгая модель (меньше ложных 'ДА')")
|
||||
print(f" - Порог 0.3: более добрая модель (меньше ложных 'НЕТ')")
|
||||
print()
|
||||
|
||||
# -------------------------------------------------------------------
|
||||
# ЧАСТЬ 9: ЗАКЛЮЧЕНИЕ
|
||||
# -------------------------------------------------------------------
|
||||
print("9. ЧТО МЫ УЗНАЛИ?")
|
||||
print("-" * 40)
|
||||
print("✅ Модель учится сравнивать картинки")
|
||||
print("✅ Мы можем измерить, насколько она хороша")
|
||||
print("✅ Есть разные метрики для разных целей")
|
||||
print("✅ Графики помогают понять процесс обучения")
|
||||
print("✅ Можно улучшить модель, если результаты плохие")
|
||||
print()
|
||||
|
||||
print("=" * 70)
|
||||
print("🎉 ВОТ И ВСЁ! ТЕПЕРЬ ТЫ ЗНАЕШЬ, КАК ОЦЕНИВАТЬ МОДЕЛЬ!")
|
||||
print("=" * 70)
|
||||
print()
|
||||
print("Следующие шаги:")
|
||||
print("1. Запусти evaluation.py чтобы увидеть реальные графики")
|
||||
print("2. Посмотри на матрицу ошибок - какие ошибки чаще?")
|
||||
print("3. Попробуй изменить порог принятия решений")
|
||||
print("4. Если нужно - переобучи модель с другими параметрами")
|
||||
917
models/SiaN-similarity/train-adv.py
Normal file
917
models/SiaN-similarity/train-adv.py
Normal file
@@ -0,0 +1,917 @@
|
||||
|
||||
import os
|
||||
import time
|
||||
from datetime import datetime
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.optim as optim
|
||||
from torch.utils.data import DataLoader
|
||||
from torch.utils.tensorboard import SummaryWriter
|
||||
from tqdm import tqdm
|
||||
|
||||
# =============================================================================
|
||||
# TRAINING LOOP WITH VISUALIZATION
|
||||
# =============================================================================
|
||||
|
||||
class SimilarityTrainer:
|
||||
def __init__(
|
||||
self,
|
||||
model: nn.Module,
|
||||
trainloader: DataLoader,
|
||||
valloader: DataLoader,
|
||||
device: torch.device,
|
||||
config: dict,
|
||||
):
|
||||
self.model = model.to(device)
|
||||
self.trainloader = trainloader
|
||||
self.valloader = valloader
|
||||
self.device = device
|
||||
self.config = config
|
||||
|
||||
self.criterion = SimilarityLoss()
|
||||
self.optimizer = optim.Adam(
|
||||
model.parameters(),
|
||||
lr=config.get('learning_rate', 2e-4),
|
||||
betas=(config.get('beta1', 0.5), config.get('beta2', 0.999))
|
||||
)
|
||||
|
||||
self.writer = None
|
||||
self.best_val_loss = float('inf')
|
||||
self.epochs_without_improvement = 0
|
||||
|
||||
# Для хранения истории метрик
|
||||
self.history = {
|
||||
'train_loss': [],
|
||||
'val_loss': [],
|
||||
'val_accuracy': [],
|
||||
'val_precision': [],
|
||||
'val_recall': [],
|
||||
'val_f1': [],
|
||||
'learning_rate': []
|
||||
}
|
||||
|
||||
def train_epoch(self, epoch: int) -> dict:
|
||||
"""Обучение на одной эпохе"""
|
||||
self.model.train()
|
||||
total_loss = 0
|
||||
total_samples = 0
|
||||
all_metrics = []
|
||||
|
||||
pbar = tqdm(self.trainloader, desc=f'Epoch {epoch}')
|
||||
for batch_idx, batch in enumerate(pbar):
|
||||
google_img = batch['google_img'].to(self.device)
|
||||
yandex_img = batch['yandex_img'].to(self.device)
|
||||
target = batch['same_domain'].float().to(self.device).unsqueeze(1)
|
||||
|
||||
self.optimizer.zero_grad()
|
||||
|
||||
# Forward pass
|
||||
output = self.model(google_img, yandex_img)
|
||||
loss = self.criterion(output, target)
|
||||
|
||||
# Backward pass
|
||||
loss.backward()
|
||||
|
||||
# Gradient clipping
|
||||
if self.config.get('grad_clip', None):
|
||||
torch.nn.utils.clip_grad_norm_(
|
||||
self.model.parameters(),
|
||||
self.config['grad_clip']
|
||||
)
|
||||
|
||||
self.optimizer.step()
|
||||
|
||||
total_loss += loss.item() * google_img.size(0)
|
||||
total_samples += google_img.size(0)
|
||||
|
||||
# Compute metrics
|
||||
if batch_idx % self.config.get('log_interval', 10) == 0:
|
||||
metrics = self.criterion.compute_metrics(output, target)
|
||||
all_metrics.append(metrics)
|
||||
pbar.set_postfix({
|
||||
'loss': f"{loss.item():.4f}",
|
||||
'acc': f"{metrics['accuracy']:.4f}"
|
||||
})
|
||||
|
||||
if self.writer:
|
||||
global_step = epoch * len(self.trainloader) + batch_idx
|
||||
self.writer.add_scalar('train/loss', loss.item(), global_step)
|
||||
self.writer.add_scalar('train/accuracy', metrics['accuracy'], global_step)
|
||||
|
||||
avg_loss = total_loss / total_samples
|
||||
|
||||
# Average metrics
|
||||
if all_metrics:
|
||||
avg_metrics = {
|
||||
key: sum(m[key] for m in all_metrics) / len(all_metrics)
|
||||
for key in all_metrics[0].keys()
|
||||
}
|
||||
else:
|
||||
avg_metrics = {}
|
||||
|
||||
return {'loss': avg_loss, **avg_metrics}
|
||||
|
||||
def validate(self) -> dict:
|
||||
"""Валидация модели"""
|
||||
self.model.eval()
|
||||
total_loss = 0
|
||||
total_samples = 0
|
||||
all_metrics = []
|
||||
|
||||
# Для ROC и confusion matrix
|
||||
all_predictions = []
|
||||
all_targets = []
|
||||
|
||||
with torch.no_grad():
|
||||
for batch in tqdm(self.valloader, desc='Validation'):
|
||||
google_img = batch['google_img'].to(self.device)
|
||||
yandex_img = batch['yandex_img'].to(self.device)
|
||||
target = batch['same_domain'].float().to(self.device).unsqueeze(1)
|
||||
|
||||
output = self.model(google_img, yandex_img)
|
||||
loss = self.criterion(output, target)
|
||||
|
||||
total_loss += loss.item() * google_img.size(0)
|
||||
total_samples += google_img.size(0)
|
||||
|
||||
metrics = self.criterion.compute_metrics(output, target)
|
||||
all_metrics.append(metrics)
|
||||
|
||||
all_predictions.append(output.cpu())
|
||||
all_targets.append(target.cpu())
|
||||
|
||||
avg_loss = total_loss / total_samples
|
||||
avg_metrics = {
|
||||
key: sum(m[key] for m in all_metrics) / len(all_metrics)
|
||||
for key in all_metrics[0].keys()
|
||||
}
|
||||
|
||||
# Concatenate all predictions and targets
|
||||
all_predictions = torch.cat(all_predictions, dim=0)
|
||||
all_targets = torch.cat(all_targets, dim=0)
|
||||
|
||||
return {
|
||||
'loss': avg_loss,
|
||||
**avg_metrics,
|
||||
'predictions': all_predictions,
|
||||
'targets': all_targets
|
||||
}
|
||||
|
||||
def train(self, num_epochs: int):
|
||||
"""Основной цикл обучения"""
|
||||
log_dir = os.path.join(self.config.get('output_dir', 'runs/similarity'))
|
||||
os.makedirs(log_dir, exist_ok=True)
|
||||
self.writer = SummaryWriter(log_dir)
|
||||
|
||||
print(f'\n{"="*70}')
|
||||
print(f'Starting training for {num_epochs} epochs')
|
||||
print(f'Logging to {log_dir}')
|
||||
print(f'{"="*70}\n')
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
for epoch in range(1, num_epochs + 1):
|
||||
epoch_start = time.time()
|
||||
print(f'\n--- Epoch {epoch}/{num_epochs} ---')
|
||||
|
||||
# Train
|
||||
train_metrics = self.train_epoch(epoch)
|
||||
|
||||
# Validate
|
||||
val_metrics = self.validate()
|
||||
|
||||
# Store history
|
||||
self.history['train_loss'].append(train_metrics['loss'])
|
||||
self.history['val_loss'].append(val_metrics['loss'])
|
||||
self.history['val_accuracy'].append(val_metrics['accuracy'])
|
||||
self.history['val_precision'].append(val_metrics['precision'])
|
||||
self.history['val_recall'].append(val_metrics['recall'])
|
||||
self.history['val_f1'].append(val_metrics['f1'])
|
||||
self.history['learning_rate'].append(
|
||||
self.optimizer.param_groups[0]['lr']
|
||||
)
|
||||
|
||||
# Print metrics
|
||||
print(f'\nTrain Loss: {train_metrics["loss"]:.4f}')
|
||||
print(f'Val Loss: {val_metrics["loss"]:.4f}')
|
||||
print(f'Val Accuracy: {val_metrics["accuracy"]:.4f}')
|
||||
print(f'Val Precision: {val_metrics["precision"]:.4f}')
|
||||
print(f'Val Recall: {val_metrics["recall"]:.4f}')
|
||||
print(f'Val F1: {val_metrics["f1"]:.4f}')
|
||||
|
||||
epoch_time = time.time() - epoch_start
|
||||
print(f'Epoch time: {epoch_time:.2f}s')
|
||||
|
||||
# TensorBoard logging
|
||||
if self.writer:
|
||||
self.writer.add_scalar('epoch/train_loss', train_metrics['loss'], epoch)
|
||||
self.writer.add_scalar('epoch/val_loss', val_metrics['loss'], epoch)
|
||||
self.writer.add_scalar('epoch/val_accuracy', val_metrics['accuracy'], epoch)
|
||||
self.writer.add_scalar('epoch/val_precision', val_metrics['precision'], epoch)
|
||||
self.writer.add_scalar('epoch/val_recall', val_metrics['recall'], epoch)
|
||||
self.writer.add_scalar('epoch/val_f1', val_metrics['f1'], epoch)
|
||||
|
||||
# Save checkpoint
|
||||
if val_metrics['loss'] < self.best_val_loss:
|
||||
self.best_val_loss = val_metrics['loss']
|
||||
self.epochs_without_improvement = 0
|
||||
self.save_checkpoint(epoch, val_metrics['loss'], is_best=True)
|
||||
print(f'✓ New best model saved with val loss: {val_metrics["loss"]:.4f}')
|
||||
else:
|
||||
self.epochs_without_improvement += 1
|
||||
if epoch % self.config.get('save_interval', 5) == 0:
|
||||
self.save_checkpoint(epoch, val_metrics['loss'], is_best=False)
|
||||
|
||||
# Early stopping
|
||||
patience = self.config.get('early_stopping_patience', 20)
|
||||
if self.epochs_without_improvement >= patience:
|
||||
print(f'\n⚠ Early stopping triggered after {patience} epochs without improvement')
|
||||
break
|
||||
|
||||
total_time = time.time() - start_time
|
||||
print(f'\n{"="*70}')
|
||||
print(f'Training completed in {total_time/60:.2f} minutes')
|
||||
print(f'Best validation loss: {self.best_val_loss:.4f}')
|
||||
print(f'{"="*70}\n')
|
||||
|
||||
self.writer.close()
|
||||
|
||||
return self.history
|
||||
|
||||
def save_checkpoint(self, epoch: int, val_loss: float, is_best: bool = False):
|
||||
"""Сохранение чекпоинта модели"""
|
||||
checkpoint_dir = os.path.join(
|
||||
self.config.get('output_dir', 'runs/similarity'),
|
||||
'checkpoints'
|
||||
)
|
||||
os.makedirs(checkpoint_dir, exist_ok=True)
|
||||
|
||||
checkpoint = {
|
||||
'epoch': epoch,
|
||||
'model_state_dict': self.model.state_dict(),
|
||||
'optimizer_state_dict': self.optimizer.state_dict(),
|
||||
'val_loss': val_loss,
|
||||
'config': self.config,
|
||||
'history': self.history
|
||||
}
|
||||
|
||||
checkpoint_path = os.path.join(checkpoint_dir, f'checkpoint_epoch{epoch}.pt')
|
||||
torch.save(checkpoint, checkpoint_path)
|
||||
|
||||
if is_best:
|
||||
best_path = os.path.join(checkpoint_dir, 'best_model.pt')
|
||||
torch.save(checkpoint, best_path)
|
||||
|
||||
def load_checkpoint(self, checkpoint_path: str):
|
||||
"""Загрузка чекпоинта"""
|
||||
checkpoint = torch.load(checkpoint_path, map_location=self.device)
|
||||
self.model.load_state_dict(checkpoint['model_state_dict'])
|
||||
self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
|
||||
if 'history' in checkpoint:
|
||||
self.history = checkpoint['history']
|
||||
return checkpoint['epoch'], checkpoint['val_loss']
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# VISUALIZATION FUNCTIONS
|
||||
# =============================================================================
|
||||
|
||||
def plot_training_history(history: dict, save_path: str = None):
|
||||
"""Построение графиков обучения"""
|
||||
fig, axes = plt.subplots(2, 3, figsize=(18, 10))
|
||||
fig.suptitle('Training History - Siamese Network для корреляции снимков',
|
||||
fontsize=16, fontweight='bold')
|
||||
|
||||
epochs = range(1, len(history['train_loss']) + 1)
|
||||
|
||||
# Loss
|
||||
axes[0, 0].plot(epochs, history['train_loss'], 'b-', label='Train Loss', linewidth=2)
|
||||
axes[0, 0].plot(epochs, history['val_loss'], 'r-', label='Val Loss', linewidth=2)
|
||||
axes[0, 0].set_xlabel('Epoch')
|
||||
axes[0, 0].set_ylabel('Loss')
|
||||
axes[0, 0].set_title('Loss Curves')
|
||||
axes[0, 0].legend()
|
||||
axes[0, 0].grid(True, alpha=0.3)
|
||||
|
||||
# Accuracy
|
||||
axes[0, 1].plot(epochs, history['val_accuracy'], 'g-', linewidth=2)
|
||||
axes[0, 1].set_xlabel('Epoch')
|
||||
axes[0, 1].set_ylabel('Accuracy')
|
||||
axes[0, 1].set_title('Validation Accuracy')
|
||||
axes[0, 1].grid(True, alpha=0.3)
|
||||
axes[0, 1].set_ylim([0, 1])
|
||||
|
||||
# F1 Score
|
||||
axes[0, 2].plot(epochs, history['val_f1'], 'm-', linewidth=2)
|
||||
axes[0, 2].set_xlabel('Epoch')
|
||||
axes[0, 2].set_ylabel('F1 Score')
|
||||
axes[0, 2].set_title('Validation F1 Score')
|
||||
axes[0, 2].grid(True, alpha=0.3)
|
||||
axes[0, 2].set_ylim([0, 1])
|
||||
|
||||
# Precision
|
||||
axes[1, 0].plot(epochs, history['val_precision'], 'c-', linewidth=2)
|
||||
axes[1, 0].set_xlabel('Epoch')
|
||||
axes[1, 0].set_ylabel('Precision')
|
||||
axes[1, 0].set_title('Validation Precision')
|
||||
axes[1, 0].grid(True, alpha=0.3)
|
||||
axes[1, 0].set_ylim([0, 1])
|
||||
|
||||
# Recall
|
||||
axes[1, 1].plot(epochs, history['val_recall'], 'y-', linewidth=2)
|
||||
axes[1, 1].set_xlabel('Epoch')
|
||||
axes[1, 1].set_ylabel('Recall')
|
||||
axes[1, 1].set_title('Validation Recall')
|
||||
axes[1, 1].grid(True, alpha=0.3)
|
||||
axes[1, 1].set_ylim([0, 1])
|
||||
|
||||
# Learning Rate
|
||||
axes[1, 2].plot(epochs, history['learning_rate'], 'k-', linewidth=2)
|
||||
axes[1, 2].set_xlabel('Epoch')
|
||||
axes[1, 2].set_ylabel('Learning Rate')
|
||||
axes[1, 2].set_title('Learning Rate Schedule')
|
||||
axes[1, 2].grid(True, alpha=0.3)
|
||||
axes[1, 2].set_yscale('log')
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
if save_path:
|
||||
plt.savefig(save_path, dpi=300, bbox_inches='tight')
|
||||
print(f'Training history plot saved to {save_path}')
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
def plot_roc_curve(predictions: torch.Tensor, targets: torch.Tensor, save_path: str = None):
|
||||
"""Построение ROC кривой"""
|
||||
from sklearn.metrics import roc_curve, auc
|
||||
|
||||
predictions_np = predictions.numpy().flatten()
|
||||
targets_np = targets.numpy().flatten()
|
||||
|
||||
fpr, tpr, thresholds = roc_curve(targets_np, predictions_np)
|
||||
roc_auc = auc(fpr, tpr)
|
||||
|
||||
plt.figure(figsize=(10, 8))
|
||||
plt.plot(fpr, tpr, color='darkorange', lw=2,
|
||||
label=f'ROC curve (AUC = {roc_auc:.3f})')
|
||||
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--', label='Random')
|
||||
plt.xlim([0.0, 1.0])
|
||||
plt.ylim([0.0, 1.05])
|
||||
plt.xlabel('False Positive Rate', fontsize=12)
|
||||
plt.ylabel('True Positive Rate', fontsize=12)
|
||||
plt.title('ROC Curve - Siamese Network', fontsize=14, fontweight='bold')
|
||||
plt.legend(loc="lower right", fontsize=12)
|
||||
plt.grid(True, alpha=0.3)
|
||||
|
||||
if save_path:
|
||||
plt.savefig(save_path, dpi=300, bbox_inches='tight')
|
||||
print(f'ROC curve saved to {save_path}')
|
||||
|
||||
plt.show()
|
||||
|
||||
return roc_auc
|
||||
|
||||
|
||||
def plot_confusion_matrix(predictions: torch.Tensor, targets: torch.Tensor,
|
||||
threshold: float = 0.5, save_path: str = None):
|
||||
"""Построение матрицы ошибок"""
|
||||
from sklearn.metrics import confusion_matrix
|
||||
|
||||
predictions_binary = (predictions.numpy().flatten() >= threshold).astype(int)
|
||||
targets_binary = (targets.numpy().flatten() >= 0.5).astype(int)
|
||||
|
||||
cm = confusion_matrix(targets_binary, predictions_binary)
|
||||
|
||||
plt.figure(figsize=(10, 8))
|
||||
plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
|
||||
plt.title('Confusion Matrix - Correlation Detection', fontsize=14, fontweight='bold')
|
||||
plt.colorbar()
|
||||
|
||||
classes = ['Different Domains', 'Same Domain']
|
||||
tick_marks = np.arange(len(classes))
|
||||
plt.xticks(tick_marks, classes, rotation=45, fontsize=12)
|
||||
plt.yticks(tick_marks, classes, fontsize=12)
|
||||
|
||||
# Add text annotations
|
||||
thresh = cm.max() / 2.
|
||||
for i in range(cm.shape[0]):
|
||||
for j in range(cm.shape[1]):
|
||||
plt.text(j, i, format(cm[i, j], 'd'),
|
||||
ha="center", va="center",
|
||||
color="white" if cm[i, j] > thresh else "black",
|
||||
fontsize=16, fontweight='bold')
|
||||
|
||||
plt.ylabel('True Label', fontsize=12)
|
||||
plt.xlabel('Predicted Label', fontsize=12)
|
||||
plt.tight_layout()
|
||||
|
||||
if save_path:
|
||||
plt.savefig(save_path, dpi=300, bbox_inches='tight')
|
||||
print(f'Confusion matrix saved to {save_path}')
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
def plot_similarity_distribution(predictions: torch.Tensor, targets: torch.Tensor,
|
||||
save_path: str = None):
|
||||
"""Распределение предсказанных значений схожести"""
|
||||
predictions_np = predictions.numpy().flatten()
|
||||
targets_np = targets.numpy().flatten()
|
||||
|
||||
same_domain = predictions_np[targets_np >= 0.5]
|
||||
diff_domain = predictions_np[targets_np < 0.5]
|
||||
|
||||
plt.figure(figsize=(12, 6))
|
||||
|
||||
plt.subplot(1, 2, 1)
|
||||
plt.hist(same_domain, bins=50, alpha=0.7, color='green', edgecolor='black', label='Same Domain')
|
||||
plt.hist(diff_domain, bins=50, alpha=0.7, color='red', edgecolor='black', label='Different Domains')
|
||||
plt.xlabel('Predicted Similarity Score', fontsize=12)
|
||||
plt.ylabel('Frequency', fontsize=12)
|
||||
plt.title('Distribution of Similarity Scores', fontsize=14, fontweight='bold')
|
||||
plt.legend(fontsize=11)
|
||||
plt.grid(True, alpha=0.3)
|
||||
|
||||
plt.subplot(1, 2, 2)
|
||||
plt.boxplot([diff_domain, same_domain], labels=['Different', 'Same'])
|
||||
plt.ylabel('Similarity Score', fontsize=12)
|
||||
plt.xlabel('Domain Match', fontsize=12)
|
||||
plt.title('Similarity Score by Domain Match', fontsize=14, fontweight='bold')
|
||||
plt.grid(True, alpha=0.3, axis='y')
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
if save_path:
|
||||
plt.savefig(save_path, dpi=300, bbox_inches='tight')
|
||||
print(f'Similarity distribution plot saved to {save_path}')
|
||||
|
||||
plt.show()
|
||||
|
||||
# Print statistics
|
||||
print(f'\n--- Similarity Score Statistics ---')
|
||||
print(f'Same Domain:')
|
||||
print(f' Mean: {same_domain.mean():.4f}')
|
||||
print(f' Std: {same_domain.std():.4f}')
|
||||
print(f' Min: {same_domain.min():.4f}')
|
||||
print(f' Max: {same_domain.max():.4f}')
|
||||
print(f'\nDifferent Domains:')
|
||||
print(f' Mean: {diff_domain.mean():.4f}')
|
||||
print(f' Std: {diff_domain.std():.4f}')
|
||||
print(f' Min: {diff_domain.min():.4f}')
|
||||
print(f' Max: {diff_domain.max():.4f}')
|
||||
|
||||
|
||||
def visualize_sample_predictions(model: nn.Module, dataset, device: torch.device,
|
||||
num_samples: int = 8, save_path: str = None):
|
||||
"""Визуализация примеров предсказаний"""
|
||||
model.eval()
|
||||
|
||||
# Get random samples
|
||||
indices = np.random.choice(len(dataset), num_samples, replace=False)
|
||||
|
||||
fig, axes = plt.subplots(num_samples, 3, figsize=(15, 5*num_samples))
|
||||
if num_samples == 1:
|
||||
axes = axes.reshape(1, -1)
|
||||
|
||||
fig.suptitle('Sample Predictions - Siamese Network для корреляции карт',
|
||||
fontsize=16, fontweight='bold')
|
||||
|
||||
with torch.no_grad():
|
||||
for idx, sample_idx in enumerate(indices):
|
||||
sample = dataset[sample_idx]
|
||||
|
||||
google_img = sample['google_img'].unsqueeze(0).to(device)
|
||||
yandex_img = sample['yandex_img'].unsqueeze(0).to(device)
|
||||
true_label = sample['same_domain'].item()
|
||||
|
||||
# Predict
|
||||
pred_similarity = model(google_img, yandex_img).item()
|
||||
pred_label = int(pred_similarity >= 0.5)
|
||||
|
||||
# Denormalize images for visualization
|
||||
google_np = google_img.squeeze(0).cpu().numpy().transpose(1, 2, 0)
|
||||
yandex_np = yandex_img.squeeze(0).cpu().numpy().transpose(1, 2, 0)
|
||||
|
||||
# Denormalize (assuming ImageNet normalization)
|
||||
mean = np.array([0.485, 0.456, 0.406])
|
||||
std = np.array([0.229, 0.224, 0.225])
|
||||
google_np = std * google_np + mean
|
||||
yandex_np = std * yandex_np + mean
|
||||
google_np = np.clip(google_np, 0, 1)
|
||||
yandex_np = np.clip(yandex_np, 0, 1)
|
||||
|
||||
# Plot Google image
|
||||
axes[idx, 0].imshow(google_np)
|
||||
axes[idx, 0].set_title('Google Map', fontsize=12, fontweight='bold')
|
||||
axes[idx, 0].axis('off')
|
||||
|
||||
# Plot Yandex image
|
||||
axes[idx, 1].imshow(yandex_np)
|
||||
axes[idx, 1].set_title('Yandex Map', fontsize=12, fontweight='bold')
|
||||
axes[idx, 1].axis('off')
|
||||
|
||||
# Plot prediction info
|
||||
axes[idx, 2].axis('off')
|
||||
|
||||
# Determine color based on correctness
|
||||
is_correct = (pred_label == true_label)
|
||||
color = 'green' if is_correct else 'red'
|
||||
result = '✓ Correct' if is_correct else '✗ Incorrect'
|
||||
|
||||
info_text = f"""
|
||||
Prediction: {pred_similarity:.4f}
|
||||
Predicted Label: {'Same' if pred_label == 1 else 'Different'}
|
||||
True Label: {'Same' if true_label == 1 else 'Different'}
|
||||
|
||||
{result}
|
||||
"""
|
||||
|
||||
axes[idx, 2].text(0.5, 0.5, info_text,
|
||||
ha='center', va='center',
|
||||
fontsize=12,
|
||||
bbox=dict(boxstyle='round', facecolor=color, alpha=0.2),
|
||||
transform=axes[idx, 2].transAxes)
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
if save_path:
|
||||
plt.savefig(save_path, dpi=300, bbox_inches='tight')
|
||||
print(f'Sample predictions saved to {save_path}')
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
def visualize_feature_space(model: nn.Module, dataloader: DataLoader,
|
||||
device: torch.device, max_samples: int = 500,
|
||||
save_path: str = None):
|
||||
"""Визуализация пространства признаков с помощью t-SNE"""
|
||||
from sklearn.manifold import TSNE
|
||||
|
||||
model.eval()
|
||||
|
||||
all_features_google = []
|
||||
all_features_yandex = []
|
||||
all_labels = []
|
||||
|
||||
with torch.no_grad():
|
||||
for i, batch in enumerate(tqdm(dataloader, desc='Extracting features')):
|
||||
if i * dataloader.batch_size >= max_samples:
|
||||
break
|
||||
|
||||
google_img = batch['google_img'].to(device)
|
||||
yandex_img = batch['yandex_img'].to(device)
|
||||
labels = batch['same_domain'].cpu().numpy()
|
||||
|
||||
# Extract features
|
||||
features_google = model.extract_features(google_img).cpu().numpy()
|
||||
features_yandex = model.extract_features(yandex_img).cpu().numpy()
|
||||
|
||||
all_features_google.append(features_google)
|
||||
all_features_yandex.append(features_yandex)
|
||||
all_labels.append(labels)
|
||||
|
||||
all_features_google = np.concatenate(all_features_google, axis=0)
|
||||
all_features_yandex = np.concatenate(all_features_yandex, axis=0)
|
||||
all_labels = np.concatenate(all_labels, axis=0)
|
||||
|
||||
# Combine features
|
||||
all_features = np.concatenate([all_features_google, all_features_yandex], axis=0)
|
||||
all_labels = np.concatenate([all_labels, all_labels], axis=0)
|
||||
|
||||
print(f'\nApplying t-SNE to {all_features.shape[0]} samples...')
|
||||
tsne = TSNE(n_components=2, random_state=42, perplexity=30)
|
||||
features_2d = tsne.fit_transform(all_features)
|
||||
|
||||
# Split back into Google and Yandex
|
||||
n_samples = len(all_labels) // 2
|
||||
features_google_2d = features_2d[:n_samples]
|
||||
features_yandex_2d = features_2d[n_samples:]
|
||||
labels = all_labels[:n_samples]
|
||||
|
||||
# Plot
|
||||
fig, axes = plt.subplots(1, 2, figsize=(20, 8))
|
||||
|
||||
# Google features
|
||||
for label in [0, 1]:
|
||||
mask = labels == label
|
||||
axes[0].scatter(
|
||||
features_google_2d[mask, 0],
|
||||
features_google_2d[mask, 1],
|
||||
c='green' if label == 1 else 'red',
|
||||
label='Same Domain' if label == 1 else 'Different Domains',
|
||||
alpha=0.6,
|
||||
s=50
|
||||
)
|
||||
axes[0].set_title('Google Maps Features (t-SNE)', fontsize=14, fontweight='bold')
|
||||
axes[0].set_xlabel('t-SNE Component 1', fontsize=12)
|
||||
axes[0].set_ylabel('t-SNE Component 2', fontsize=12)
|
||||
axes[0].legend(fontsize=11)
|
||||
axes[0].grid(True, alpha=0.3)
|
||||
|
||||
# Yandex features
|
||||
for label in [0, 1]:
|
||||
mask = labels == label
|
||||
axes[1].scatter(
|
||||
features_yandex_2d[mask, 0],
|
||||
features_yandex_2d[mask, 1],
|
||||
c='green' if label == 1 else 'red',
|
||||
label='Same Domain' if label == 1 else 'Different Domains',
|
||||
alpha=0.6,
|
||||
s=50
|
||||
)
|
||||
axes[1].set_title('Yandex Maps Features (t-SNE)', fontsize=14, fontweight='bold')
|
||||
axes[1].set_xlabel('t-SNE Component 1', fontsize=12)
|
||||
axes[1].set_ylabel('t-SNE Component 2', fontsize=12)
|
||||
axes[1].legend(fontsize=11)
|
||||
axes[1].grid(True, alpha=0.3)
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
if save_path:
|
||||
plt.savefig(save_path, dpi=300, bbox_inches='tight')
|
||||
print(f'Feature space visualization saved to {save_path}')
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
def generate_correlation_heatmap(model: nn.Module, dataloader: DataLoader,
|
||||
device: torch.device, num_samples: int = 20,
|
||||
save_path: str = None):
|
||||
"""Создание тепловой карты корреляций между снимками"""
|
||||
model.eval()
|
||||
|
||||
# Collect samples
|
||||
google_images = []
|
||||
yandex_images = []
|
||||
labels = []
|
||||
|
||||
with torch.no_grad():
|
||||
for i, batch in enumerate(dataloader):
|
||||
if len(google_images) >= num_samples:
|
||||
break
|
||||
|
||||
google_img = batch['google_img'].to(device)
|
||||
yandex_img = batch['yandex_img'].to(device)
|
||||
label = batch['same_domain']
|
||||
|
||||
google_images.append(google_img[:1])
|
||||
yandex_images.append(yandex_img[:1])
|
||||
labels.append(label[:1].item())
|
||||
|
||||
google_images = torch.cat(google_images[:num_samples], dim=0)
|
||||
yandex_images = torch.cat(yandex_images[:num_samples], dim=0)
|
||||
|
||||
# Compute similarity matrix
|
||||
similarity_matrix = np.zeros((num_samples, num_samples))
|
||||
|
||||
with torch.no_grad():
|
||||
for i in tqdm(range(num_samples), desc='Computing correlations'):
|
||||
for j in range(num_samples):
|
||||
google_i = google_images[i:i+1]
|
||||
yandex_j = yandex_images[j:j+1]
|
||||
similarity = model(google_i, yandex_j).item()
|
||||
similarity_matrix[i, j] = similarity
|
||||
|
||||
# Plot heatmap
|
||||
plt.figure(figsize=(14, 12))
|
||||
im = plt.imshow(similarity_matrix, cmap='RdYlGn', aspect='auto', vmin=0, vmax=1)
|
||||
|
||||
plt.colorbar(im, label='Similarity Score', fraction=0.046, pad=0.04)
|
||||
plt.title('Correlation Heatmap: Google vs Yandex Maps\n(Матрица корреляций снимков)',
|
||||
fontsize=16, fontweight='bold', pad=20)
|
||||
plt.xlabel('Yandex Map Index', fontsize=12)
|
||||
plt.ylabel('Google Map Index', fontsize=12)
|
||||
|
||||
# Add grid
|
||||
plt.grid(True, which='both', color='gray', linestyle='-', linewidth=0.5, alpha=0.3)
|
||||
|
||||
# Add text annotations for diagonal (same pairs)
|
||||
for i in range(min(num_samples, 10)): # Annotate first 10 for readability
|
||||
if labels[i] == 1: # True match
|
||||
plt.text(i, i, '✓', ha='center', va='center',
|
||||
color='white', fontsize=12, fontweight='bold')
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
if save_path:
|
||||
plt.savefig(save_path, dpi=300, bbox_inches='tight')
|
||||
print(f'Correlation heatmap saved to {save_path}')
|
||||
|
||||
plt.show()
|
||||
|
||||
# Print statistics
|
||||
diagonal = np.diag(similarity_matrix)
|
||||
off_diagonal = similarity_matrix[~np.eye(num_samples, dtype=bool)]
|
||||
|
||||
print(f'\n--- Correlation Statistics ---')
|
||||
print(f'Diagonal (matched pairs):')
|
||||
print(f' Mean: {diagonal.mean():.4f}')
|
||||
print(f' Std: {diagonal.std():.4f}')
|
||||
print(f'\nOff-diagonal (mismatched pairs):')
|
||||
print(f' Mean: {off_diagonal.mean():.4f}')
|
||||
print(f' Std: {off_diagonal.std():.4f}')
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# MAIN TRAINING SCRIPT
|
||||
# =============================================================================
|
||||
|
||||
def main():
|
||||
"""Основная функция обучения"""
|
||||
|
||||
# Configuration
|
||||
config_dict = config.copy()
|
||||
if isinstance(config_dict.get('image_size'), list):
|
||||
config_dict['image_size'] = tuple(config_dict['image_size'])
|
||||
|
||||
# Device
|
||||
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
||||
print(f'\n{"="*70}')
|
||||
print(f'Siamese Network Training for Map Correlation')
|
||||
print(f'Обучение сиамской сети для корреляции снимков')
|
||||
print(f'{"="*70}')
|
||||
print(f'Using device: {device}')
|
||||
if torch.cuda.is_available():
|
||||
print(f'GPU: {torch.cuda.get_device_name(0)}')
|
||||
print(f'GPU Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB')
|
||||
|
||||
# Create data loaders
|
||||
print(f'\n{"="*70}')
|
||||
print('Creating data loaders...')
|
||||
print(f'{"="*70}')
|
||||
|
||||
train_loader, val_loader = create_data_loaders(
|
||||
root_dir=config_dict['data_dir'],
|
||||
batch_size=config_dict['batch_size'],
|
||||
train_split=config_dict['train_split'],
|
||||
num_workers=config_dict['num_workers'],
|
||||
image_size=config_dict['image_size'],
|
||||
augment_train=True,
|
||||
augment_val=False,
|
||||
device=device
|
||||
)
|
||||
|
||||
print(f'Train batches: {len(train_loader)}')
|
||||
print(f'Val batches: {len(val_loader)}')
|
||||
print(f'Train samples: {len(train_loader.dataset)}')
|
||||
print(f'Val samples: {len(val_loader.dataset)}')
|
||||
|
||||
# Create model
|
||||
print(f'\n{"="*70}')
|
||||
print('Creating model...')
|
||||
print(f'{"="*70}')
|
||||
|
||||
model = create_similarity_model(
|
||||
model_type='backbone',
|
||||
input_size=config_dict['image_size'][0] if isinstance(config_dict['image_size'], (tuple, list)) else config_dict['image_size'],
|
||||
input_channels=3,
|
||||
backbone_name='resnet18',
|
||||
pretrained=True,
|
||||
dropout_rate=0.3,
|
||||
use_batch_norm=True
|
||||
)
|
||||
|
||||
total_params = sum(p.numel() for p in model.parameters())
|
||||
trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
|
||||
print(f'Total parameters: {total_params:,}')
|
||||
print(f'Trainable parameters: {trainable_params:,}')
|
||||
|
||||
# Create trainer
|
||||
trainer = SimilarityTrainer(
|
||||
model=model,
|
||||
trainloader=train_loader,
|
||||
valloader=val_loader,
|
||||
device=device,
|
||||
config=config_dict
|
||||
)
|
||||
|
||||
# Train model
|
||||
print(f'\n{"="*70}')
|
||||
print('Starting training...')
|
||||
print(f'{"="*70}')
|
||||
|
||||
history = trainer.train(config_dict['epochs'])
|
||||
|
||||
# =============================================================================
|
||||
# VISUALIZATION AND RESULTS
|
||||
# =============================================================================
|
||||
|
||||
print(f'\n{"="*70}')
|
||||
print('Generating visualizations...')
|
||||
print(f'{"="*70}')
|
||||
|
||||
output_dir = config_dict.get('output_dir', 'runs/similarity')
|
||||
vis_dir = os.path.join(output_dir, 'visualizations')
|
||||
os.makedirs(vis_dir, exist_ok=True)
|
||||
|
||||
# 1. Training history
|
||||
print('\n1. Plotting training history...')
|
||||
plot_training_history(
|
||||
history,
|
||||
save_path=os.path.join(vis_dir, 'training_history.png')
|
||||
)
|
||||
|
||||
# 2. Validation metrics
|
||||
print('\n2. Computing validation predictions...')
|
||||
trainer.model.eval()
|
||||
val_predictions = []
|
||||
val_targets = []
|
||||
|
||||
with torch.no_grad():
|
||||
for batch in tqdm(val_loader, desc='Validation'):
|
||||
google_img = batch['google_img'].to(device)
|
||||
yandex_img = batch['yandex_img'].to(device)
|
||||
target = batch['same_domain'].float().unsqueeze(1)
|
||||
|
||||
output = trainer.model(google_img, yandex_img)
|
||||
val_predictions.append(output.cpu())
|
||||
val_targets.append(target.cpu())
|
||||
|
||||
val_predictions = torch.cat(val_predictions, dim=0)
|
||||
val_targets = torch.cat(val_targets, dim=0)
|
||||
|
||||
# 3. ROC curve
|
||||
print('\n3. Plotting ROC curve...')
|
||||
roc_auc = plot_roc_curve(
|
||||
val_predictions,
|
||||
val_targets,
|
||||
save_path=os.path.join(vis_dir, 'roc_curve.png')
|
||||
)
|
||||
print(f'ROC AUC Score: {roc_auc:.4f}')
|
||||
|
||||
# 4. Confusion matrix
|
||||
print('\n4. Plotting confusion matrix...')
|
||||
plot_confusion_matrix(
|
||||
val_predictions,
|
||||
val_targets,
|
||||
threshold=0.5,
|
||||
save_path=os.path.join(vis_dir, 'confusion_matrix.png')
|
||||
)
|
||||
|
||||
# 5. Similarity distribution
|
||||
print('\n5. Plotting similarity distribution...')
|
||||
plot_similarity_distribution(
|
||||
val_predictions,
|
||||
val_targets,
|
||||
save_path=os.path.join(vis_dir, 'similarity_distribution.png')
|
||||
)
|
||||
|
||||
# 6. Sample predictions
|
||||
print('\n6. Visualizing sample predictions...')
|
||||
visualize_sample_predictions(
|
||||
trainer.model,
|
||||
val_loader.dataset,
|
||||
device,
|
||||
num_samples=8,
|
||||
save_path=os.path.join(vis_dir, 'sample_predictions.png')
|
||||
)
|
||||
|
||||
# 7. Feature space visualization
|
||||
print('\n7. Visualizing feature space (t-SNE)...')
|
||||
visualize_feature_space(
|
||||
trainer.model,
|
||||
val_loader,
|
||||
device,
|
||||
max_samples=500,
|
||||
save_path=os.path.join(vis_dir, 'feature_space_tsne.png')
|
||||
)
|
||||
|
||||
# 8. Correlation heatmap
|
||||
print('\n8. Generating correlation heatmap...')
|
||||
generate_correlation_heatmap(
|
||||
trainer.model,
|
||||
val_loader,
|
||||
device,
|
||||
num_samples=20,
|
||||
save_path=os.path.join(vis_dir, 'correlation_heatmap.png')
|
||||
)
|
||||
|
||||
# =============================================================================
|
||||
# FINAL RESULTS SUMMARY
|
||||
# =============================================================================
|
||||
|
||||
print(f'\n{"="*70}')
|
||||
print('FINAL RESULTS SUMMARY')
|
||||
print('ИТОГОВЫЕ РЕЗУЛЬТАТЫ')
|
||||
print(f'{"="*70}')
|
||||
|
||||
print(f'\nBest Validation Loss: {trainer.best_val_loss:.4f}')
|
||||
print(f'Final Validation Accuracy: {history["val_accuracy"][-1]:.4f}')
|
||||
print(f'Final Validation F1 Score: {history["val_f1"][-1]:.4f}')
|
||||
print(f'Final Validation Precision: {history["val_precision"][-1]:.4f}')
|
||||
print(f'Final Validation Recall: {history["val_recall"][-1]:.4f}')
|
||||
print(f'ROC AUC Score: {roc_auc:.4f}')
|
||||
|
||||
print(f'\nCheckpoints saved to: {os.path.join(output_dir, "checkpoints")}')
|
||||
print(f'Visualizations saved to: {vis_dir}')
|
||||
|
||||
print(f'\n{"="*70}')
|
||||
print('Training and visualization completed successfully!')
|
||||
print('Обучение и визуализация завершены успешно!')
|
||||
print(f'{"="*70}\n')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@@ -2,7 +2,6 @@
|
||||
Training script for image similarity estimation.
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import time
|
||||
from datetime import datetime
|
||||
@@ -10,7 +9,7 @@ from datetime import datetime
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.optim as optim
|
||||
from dataloader import create_data_loaders
|
||||
from dataloader import config, create_data_loaders
|
||||
from model import SimilarityCNN, SimilarityLoss, create_similarity_model
|
||||
from torch.utils.data import DataLoader
|
||||
from torch.utils.tensorboard import SummaryWriter
|
||||
@@ -191,51 +190,23 @@ class SimilarityTrainer:
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Train similarity estimation model")
|
||||
parser.add_argument(
|
||||
"--data_dir",
|
||||
type=str,
|
||||
default=r"C:\Users\admin\Projects\autopilot\datasets\ya_go_maps\images",
|
||||
)
|
||||
parser.add_argument("--batch_size", type=int, default=32)
|
||||
parser.add_argument("--epochs", type=int, default=100)
|
||||
parser.add_argument("--learning_rate", type=float, default=2e-4)
|
||||
parser.add_argument("--image_size", type=int, default=256)
|
||||
parser.add_argument("--train_split", type=float, default=0.8)
|
||||
parser.add_argument("--output_dir", type=str, default="runs/similarity")
|
||||
parser.add_argument("--num_workers", type=int, default=0)
|
||||
parser.add_argument(
|
||||
"--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu"
|
||||
)
|
||||
# Use config from dataloader.py
|
||||
config_dict = config.copy()
|
||||
|
||||
args = parser.parse_args()
|
||||
# Ensure image_size is tuple
|
||||
if isinstance(config_dict.get("image_size"), list):
|
||||
config_dict["image_size"] = tuple(config_dict["image_size"])
|
||||
|
||||
config = {
|
||||
"data_dir": args.data_dir,
|
||||
"batch_size": args.batch_size,
|
||||
"epochs": args.epochs,
|
||||
"learning_rate": args.learning_rate,
|
||||
"image_size": (args.image_size, args.image_size),
|
||||
"train_split": args.train_split,
|
||||
"output_dir": args.output_dir,
|
||||
"num_workers": args.num_workers,
|
||||
"log_interval": 10,
|
||||
"save_interval": 5,
|
||||
"early_stopping_patience": 20,
|
||||
"beta1": 0.5,
|
||||
"beta2": 0.999,
|
||||
}
|
||||
|
||||
device = torch.device(args.device)
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
print(f"Using device: {device}")
|
||||
|
||||
print("Creating data loaders...")
|
||||
train_loader, val_loader = create_data_loaders(
|
||||
root_dir=config["data_dir"],
|
||||
batch_size=config["batch_size"],
|
||||
train_split=config["train_split"],
|
||||
num_workers=config["num_workers"],
|
||||
image_size=config["image_size"],
|
||||
root_dir=config_dict["data_dir"],
|
||||
batch_size=config_dict["batch_size"],
|
||||
train_split=config_dict["train_split"],
|
||||
num_workers=config_dict["num_workers"],
|
||||
image_size=config_dict["image_size"],
|
||||
augment_train=True,
|
||||
augment_val=False,
|
||||
device=device,
|
||||
@@ -247,7 +218,9 @@ def main():
|
||||
print("Creating model...")
|
||||
model = create_similarity_model(
|
||||
model_type="cnn",
|
||||
input_size=config["image_size"],
|
||||
input_size=config_dict["image_size"][0]
|
||||
if isinstance(config_dict["image_size"], (tuple, list))
|
||||
else config_dict["image_size"],
|
||||
input_channels=3,
|
||||
hidden_channels=64,
|
||||
num_blocks=4,
|
||||
@@ -262,11 +235,11 @@ def main():
|
||||
train_loader=train_loader,
|
||||
val_loader=val_loader,
|
||||
device=device,
|
||||
config=config,
|
||||
config=config_dict,
|
||||
)
|
||||
|
||||
print("Starting training...")
|
||||
trainer.train(config["epochs"])
|
||||
trainer.train(config_dict["epochs"])
|
||||
|
||||
print("Training completed!")
|
||||
|
||||
|
||||
Reference in New Issue
Block a user