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# !/usr/bin/env python3
# Add this comprehensive test function to your url_dataset.py file
# Replace the existing comprehensive test with this fixed version
def run_comprehensive_test():
"""Run comprehensive test suite for url_dataset.py"""
print("URL_DATASET.PY COMPREHENSIVE TEST SUITE")
print("=" * 60)
print("Testing all classes and functions in url_dataset.py")
import tempfile
import json
import hashlib
from unittest.mock import patch, MagicMock
from io import BytesIO
import requests
from collections import Counter
from torchvision import transforms
tests_passed = 0
tests_failed = 0
failed_tests = []
def run_test(test_name, test_func):
nonlocal tests_passed, tests_failed, failed_tests
print(f"\n{'=' * 60}")
print(f"Running: {test_name}")
print(f"{'=' * 60}")
try:
test_func()
tests_passed += 1
print(f"✅ PASSED: {test_name}")
except Exception as e:
tests_failed += 1
failed_tests.append(f"{test_name}: {str(e)}")
print(f"❌ FAILED: {test_name}")
print(f"Error: {str(e)}")
def create_mock_data(num_items=10):
"""Create mock JSON data"""
mock_data = []
decades = ['1960s', '1970s', '1980s', '1990s', '2000s']
for i in range(num_items):
mock_data.append({
"id": f"test_id_{i}",
"product_id": f"product_{i % 3}",
"name": f"Test Product {i}",
"decade": decades[i % 5],
"url": f"https://example.com/image_{i}.jpg",
"classification": f"test_class_{i % 2}",
"makers": f"test_maker_{i % 2}",
"country": f"test_country_{i % 3}"
})
return mock_data
def create_mock_image(size=(200, 200)):
"""Create mock PIL image"""
array = np.random.randint(0, 255, (*size, 3), dtype=np.uint8)
return Image.fromarray(array)
def test_base_dataset():
"""Test BaseDataset functionality"""
print("Testing BaseDataset...")
with tempfile.TemporaryDirectory() as temp_dir:
temp_path = Path(temp_dir)
split_file = temp_path / "test.json"
mock_data = create_mock_data(10)
with open(split_file, 'w') as f:
json.dump(mock_data, f)
dataset = BaseDataset(str(split_file))
print(f"✓ BaseDataset initialized with {len(dataset)} items")
print(f"✓ Number of classes: {dataset.num_classes}")
print(f"✓ Class names: {dataset.decades}")
assert len(dataset) == 10
assert dataset.num_classes == 5
assert dataset.decades == ['1960s', '1970s', '1980s', '1990s', '2000s']
labels = dataset.get_labels()
print(f"✓ Labels: {labels}")
assert len(labels) == 10
metadata = dataset.get_metadata(0)
expected_keys = ['id', 'product_id', 'name', 'decade', 'url', 'classification', 'makers', 'country']
for key in expected_keys:
assert key in metadata
print(f"✓ Metadata keys: {list(metadata.keys())}")
def test_url_dataset_mock():
"""Test URLDataset with mocked requests"""
print("Testing URLDataset with mocked requests...")
with tempfile.TemporaryDirectory() as temp_dir:
temp_path = Path(temp_dir)
cache_dir = temp_path / "cache"
split_file = temp_path / "test.json"
mock_data = create_mock_data(5)
with open(split_file, 'w') as f:
json.dump(mock_data, f)
# Create transform to ensure tensor output
test_transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor()
])
mock_image = create_mock_image((300, 300))
mock_response = MagicMock()
mock_response.content = BytesIO()
mock_image.save(mock_response.content, 'JPEG')
mock_response.content = mock_response.content.getvalue()
mock_response.raise_for_status = MagicMock()
with patch('requests.get', return_value=mock_response):
dataset = URLDataset(
split_file=str(split_file),
cache_dir=str(cache_dir),
transform=test_transform, # Provide transform
max_retries=2,
timeout=5,
fallback_on_error=True
)
print(f"✓ URLDataset initialized with {len(dataset)} items")
print(f"✓ Cache directory: {dataset.cache_dir}")
test_url = "https://example.com/test.jpg"
cache_path = dataset._get_cache_path(test_url)
print(f"✓ Cache path generation: {cache_path.name}")
# Test image loading
image, label, metadata = dataset[0]
assert isinstance(image, torch.Tensor)
assert image.dtype == torch.float32
assert 0 <= label < 5
assert isinstance(metadata, dict)
print(f"✓ First item loaded: shape={image.shape}, label={label}, decade={metadata['decade']}")
# Test statistics
stats = dataset.get_statistics()
print(f"✓ Statistics: {stats}")
def test_url_dataset_fallback():
"""Test URLDataset fallback behavior"""
print("Testing URLDataset fallback behavior...")
with tempfile.TemporaryDirectory() as temp_dir:
temp_path = Path(temp_dir)
cache_dir = temp_path / "cache"
split_file = temp_path / "test.json"
mock_data = create_mock_data(3)
with open(split_file, 'w') as f:
json.dump(mock_data, f)
# Create transform
test_transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor()
])
with patch('requests.get', side_effect=requests.exceptions.ConnectionError("Mock connection error")):
dataset = URLDataset(
split_file=str(split_file),
cache_dir=str(cache_dir),
transform=test_transform, # Provide transform
max_retries=1,
timeout=1,
fallback_on_error=True
)
# Should use placeholder image
image, label, metadata = dataset[0]
assert isinstance(image, torch.Tensor)
print(f"✓ Fallback image loaded: shape={image.shape}")
stats = dataset.get_statistics()
assert stats['failures'] > 0
print(f"✓ Failure tracked in statistics: {stats['failures']} failures")
def test_cached_dataset():
"""Test CachedDataset functionality"""
print("Testing CachedDataset...")
with tempfile.TemporaryDirectory() as temp_dir:
temp_path = Path(temp_dir)
images_dir = temp_path / "images"
images_dir.mkdir()
split_file = temp_path / "test.json"
mock_data = create_mock_data(5)
with open(split_file, 'w') as f:
json.dump(mock_data, f)
# Create cached images for first 3 items
for i in range(3):
url = mock_data[i]['url']
url_hash = hashlib.md5(url.encode()).hexdigest()
cache_path = images_dir / f"{url_hash}.jpg"
mock_image = create_mock_image((200, 200))
mock_image.save(cache_path, 'JPEG')
print(f"✓ Created {len(list(images_dir.glob('*.jpg')))} cached images")
# Create transform
test_transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor()
])
dataset = CachedDataset(
split_file=str(split_file),
images_dir=str(images_dir),
transform=test_transform, # Provide transform
verify_images=True
)
print(f"✓ CachedDataset initialized with {len(dataset)} valid images")
assert len(dataset) == 3
image, label, metadata = dataset[0]
assert isinstance(image, torch.Tensor)
assert 0 <= label < 5
print(f"✓ Cached item loaded: shape={image.shape}, label={label}")
def test_subset_creation():
"""Test subset creation"""
print("Testing create_subset_dataset...")
with tempfile.TemporaryDirectory() as temp_dir:
temp_path = Path(temp_dir)
split_file = temp_path / "test.json"
mock_data = create_mock_data(50)
with open(split_file, 'w') as f:
json.dump(mock_data, f)
original_dataset = BaseDataset(str(split_file))
original_size = len(original_dataset)
print(f"✓ Original dataset size: {original_size}")
subset_dataset = create_subset_dataset(original_dataset, fraction=0.2, seed=42)
subset_size = len(subset_dataset)
print(f"✓ Subset dataset size: {subset_size}")
print(f"✓ Subset fraction: {subset_size / original_size:.2f}")
assert subset_size >= 5 # At least 1 from each class
assert subset_size <= original_size
subset_labels = subset_dataset.get_labels()
unique_labels = set(subset_labels)
print(f"✓ Subset has {len(unique_labels)} unique classes: {unique_labels}")
def test_download_images():
"""Test bulk download functionality"""
print("Testing download_dataset_images with mocked requests...")
with tempfile.TemporaryDirectory() as temp_dir:
temp_path = Path(temp_dir)
cache_dir = temp_path / "cache"
split_files = []
for split_name in ["train", "val"]:
split_file = temp_path / f"{split_name}.json"
mock_data = create_mock_data(5)
# Make URLs unique across splits
for i, item in enumerate(mock_data):
item['url'] = f"https://example.com/{split_name}_image_{i}.jpg"
with open(split_file, 'w') as f:
json.dump(mock_data, f)
split_files.append(str(split_file))
mock_image = create_mock_image((200, 200))
mock_response = MagicMock()
mock_response.content = BytesIO()
mock_image.save(mock_response.content, 'JPEG')
mock_response.content = mock_response.content.getvalue()
mock_response.raise_for_status = MagicMock()
with patch('requests.get', return_value=mock_response):
results = download_dataset_images(
split_files=split_files,
cache_dir=str(cache_dir),
num_workers=2,
skip_existing=True
)
print(f"✓ Download results: {results}")
expected_keys = ['cached', 'downloaded', 'failed']
for key in expected_keys:
assert key in results
cached_images = list(cache_dir.glob('*.jpg'))
print(f"✓ Created {len(cached_images)} cached image files")
def test_edge_cases():
"""Test edge cases"""
print("Testing edge cases...")
with tempfile.TemporaryDirectory() as temp_dir:
temp_path = Path(temp_dir)
# Test empty dataset
empty_split_file = temp_path / "empty.json"
with open(empty_split_file, 'w') as f:
json.dump([], f)
empty_dataset = BaseDataset(str(empty_split_file))
assert len(empty_dataset) == 0
print("✓ Empty dataset handled correctly")
# Test malformed JSON
try:
malformed_split_file = temp_path / "malformed.json"
with open(malformed_split_file, 'w') as f:
f.write("invalid json")
BaseDataset(str(malformed_split_file))
assert False, "Should have raised exception"
except json.JSONDecodeError:
print("✓ Malformed JSON handled correctly")
# Test URLDataset with no fallback
split_file = temp_path / "test.json"
mock_data = create_mock_data(2)
with open(split_file, 'w') as f:
json.dump(mock_data, f)
test_transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor()
])
with patch('requests.get', side_effect=Exception("Mock error")):
dataset = URLDataset(
split_file=str(split_file),
transform=test_transform,
fallback_on_error=False,
max_retries=1
)
try:
image, label, metadata = dataset[0]
assert False, "Should have raised exception"
except (ValueError, Exception):
print("✓ No-fallback error handling works correctly")
def test_performance():
"""Test performance"""
print("Testing performance...")
with tempfile.TemporaryDirectory() as temp_dir:
temp_path = Path(temp_dir)
split_file = temp_path / "perf_test.json"
mock_data = create_mock_data(100)
with open(split_file, 'w') as f:
json.dump(mock_data, f)
import time
start_time = time.time()
dataset = BaseDataset(str(split_file))
init_time = time.time() - start_time
print(f"✓ BaseDataset initialization: {init_time:.3f}s for {len(dataset)} items")
start_time = time.time()
labels = dataset.get_labels()
label_time = time.time() - start_time
print(f"✓ Label extraction: {label_time:.3f}s for {len(labels)} labels")
start_time = time.time()
for i in range(min(10, len(dataset))):
metadata = dataset.get_metadata(i)
metadata_time = time.time() - start_time
print(f"✓ Metadata extraction: {metadata_time:.3f}s for 10 items")
assert init_time < 1.0
assert label_time < 0.1
# Run all tests
tests = [
("BaseDataset Functionality", test_base_dataset),
("URLDataset with Mocked Requests", test_url_dataset_mock),
("URLDataset Fallback Behavior", test_url_dataset_fallback),
("CachedDataset Functionality", test_cached_dataset),
("Subset Creation", test_subset_creation),
("Bulk Download with Mocked Requests", test_download_images),
("Edge Cases and Error Handling", test_edge_cases),
("Performance Testing", test_performance),
]
for test_name, test_func in tests:
run_test(test_name, test_func)
# Print summary
print(f"\n{'=' * 60}")
print(f"TEST SUMMARY")
print(f"{'=' * 60}")
print(f"Total tests: {len(tests)}")
print(f"✅ Passed: {tests_passed}")
print(f"❌ Failed: {tests_failed}")
if failed_tests:
print(f"\nFailed tests:")
for failure in failed_tests:
print(f" - {failure}")
success = tests_failed == 0
if success:
print(f"\n🎉 ALL TESTS PASSED! url_dataset.py is working correctly.")
else:
print(f"\n❌ SOME TESTS FAILED! Check the issues above.")
return success
# Update the main section to include the comprehensive test option
if __name__ == "__main__":
import sys
if len(sys.argv) > 1 and sys.argv[1] == "--comprehensive":
# Run comprehensive test
run_comprehensive_test()
else:
# Run the quick test (your existing main code)
# ... (your existing main code here)
pass |