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| """ | |
| Test script for data_loader module. | |
| Run from project root: | |
| conda activate ai_engg | |
| python scripts/test_data_loader.py | |
| """ | |
| import sys | |
| from pathlib import Path | |
| # Add project root to path | |
| sys.path.insert(0, str(Path(__file__).parent.parent)) | |
| from scripts.data_loader import MnistDataloader | |
| def test_data_loader(): | |
| """Test MNIST data loader with actual files.""" | |
| print("Testing MNIST Data Loader...") | |
| print("-" * 50) | |
| # Note: Files need to be uncompressed first | |
| # If .gz files exist, uncompress with: gunzip data/raw/*.gz | |
| base_path = Path(__file__).parent.parent / "data" / "raw" | |
| # Try to find uncompressed files | |
| train_images = base_path / "train-images.idx3-ubyte" | |
| train_labels = base_path / "train-labels.idx1-ubyte" | |
| test_images = base_path / "t10k-images.idx3-ubyte" | |
| test_labels = base_path / "t10k-labels.idx1-ubyte" | |
| # Check if files exist | |
| missing_files = [] | |
| for filepath in [train_images, train_labels, test_images, test_labels]: | |
| if not filepath.exists(): | |
| missing_files.append(str(filepath)) | |
| if missing_files: | |
| print("⚠️ Missing uncompressed data files:") | |
| for f in missing_files: | |
| print(f" - {f}") | |
| print("\nTo uncompress .gz files, run:") | |
| print(" cd data/raw && gunzip *.gz") | |
| return False | |
| try: | |
| # Initialize loader | |
| loader = MnistDataloader( | |
| str(train_images), | |
| str(train_labels), | |
| str(test_images), | |
| str(test_labels) | |
| ) | |
| print("✓ Loader initialized successfully") | |
| # Load data | |
| print("\nLoading MNIST dataset...") | |
| (x_train, y_train), (x_test, y_test) = loader.load_data() | |
| # Verify shapes | |
| print(f"\n✓ Training set: {len(x_train):,} images, {len(y_train):,} labels") | |
| print(f"✓ Test set: {len(x_test):,} images, {len(y_test):,} labels") | |
| # Convert first image to numpy array to check | |
| import numpy as np | |
| first_img = np.array(x_train[0]) | |
| print(f"\n✓ Image shape: {first_img.shape}") | |
| print(f"✓ Image dtype: {first_img.dtype}") | |
| print(f"✓ Label type: {type(y_train[0])}") | |
| # Verify label range | |
| unique_labels = set(y_train + y_test) | |
| print(f"\n✓ Unique labels: {sorted(unique_labels)}") | |
| # Verify pixel value range (convert to numpy for analysis) | |
| sample_images = [np.array(img) for img in x_train[:100]] | |
| max_val = max(img.max() for img in sample_images) | |
| min_val = min(img.min() for img in sample_images) | |
| print(f"✓ Pixel value range (sample): [{min_val}, {max_val}]") | |
| print("\n" + "=" * 50) | |
| print("✅ All tests passed!") | |
| print("=" * 50) | |
| return True | |
| except Exception as e: | |
| print(f"\n❌ Error: {e}") | |
| import traceback | |
| traceback.print_exc() | |
| return False | |
| if __name__ == "__main__": | |
| success = test_data_loader() | |
| sys.exit(0 if success else 1) | |