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"""
Evaluation script for CIFAR-10 CNN
"""
import os
import torch
from tqdm import tqdm
import config
from model import get_model
from data_loader import get_data_loaders
from utils import (
load_checkpoint, plot_confusion_matrix,
print_classification_report, visualize_predictions
)
def evaluate():
"""
Evaluate the trained model
"""
# Create plots directory
os.makedirs(config.PLOTS_DIR, exist_ok=True)
# Get data loaders
print("Loading CIFAR-10 dataset...")
_, test_loader = get_data_loaders()
print(f"Test samples: {len(test_loader.dataset)}")
# Create model
print(f"\nLoading model from {config.BEST_MODEL_PATH}")
model = get_model(num_classes=config.NUM_CLASSES, device=config.DEVICE)
# Load checkpoint
if not os.path.exists(config.BEST_MODEL_PATH):
print(f"Error: Model checkpoint not found at {config.BEST_MODEL_PATH}")
print("Please train the model first using train.py")
return
epoch, accuracy = load_checkpoint(model, None, config.BEST_MODEL_PATH)
print(f"Loaded model from epoch {epoch + 1} with accuracy: {accuracy:.2f}%")
# Evaluate
model.eval()
correct = 0
total = 0
all_predictions = []
all_labels = []
print("\nEvaluating model...")
with torch.no_grad():
pbar = tqdm(test_loader, desc='Evaluating')
for inputs, labels in pbar:
inputs, labels = inputs.to(config.DEVICE), labels.to(config.DEVICE)
# Forward pass
outputs = model(inputs)
_, predicted = outputs.max(1)
# Statistics
total += labels.size(0)
correct += predicted.eq(labels).sum().item()
# Store predictions and labels
all_predictions.extend(predicted.cpu().numpy())
all_labels.extend(labels.cpu().numpy())
# Update progress bar
pbar.set_postfix({'acc': f'{100. * correct / total:.2f}%'})
# Calculate final accuracy
final_accuracy = 100. * correct / total
# Print results
print("\n" + "=" * 80)
print(f"Final Test Accuracy: {final_accuracy:.2f}%")
print(f"Correct predictions: {correct}/{total}")
print("=" * 80)
# Print classification report
print_classification_report(all_labels, all_predictions)
# Plot confusion matrix
print("\nGenerating confusion matrix...")
cm_path = os.path.join(config.PLOTS_DIR, 'confusion_matrix.png')
plot_confusion_matrix(all_labels, all_predictions, cm_path)
print(f"Confusion matrix saved to {cm_path}")
# Visualize predictions
print("\nGenerating prediction visualizations...")
visualize_predictions(model, test_loader, config.DEVICE, num_images=16)
print("\nEvaluation completed!")
if __name__ == '__main__':
evaluate()
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