Testing with Mock Models

This document explains how to use the mock model fixture to write fast unit tests that don't require loading the actual model.

Mock Model Fixture

A mock_model fixture has been added to conftest.py that creates a lightweight mock of the MultimodalSummaryModel class. This fixture:

Does not load any actual models (super fast)
Mocks all necessary methods (processor, tokenizer, model.generate, etc.)
Returns realistic tensor shapes (so the code doesn't crash)
Can be used for fast unit tests that don't need actual model inference

Usage

Simply use mock_model instead of model in your test fixtures:

def test_my_feature(mock_model):
    detector = ImageSummaryDetector(summary_model=mock_model, subdict={})
    # Your test code here
    pass

When to Use Mock vs Real Model

Use `mock_model` when:

Testing utility functions (like _clean_list_of_questions)
Testing input validation logic
Testing data processing methods
Testing class initialization
Any test that doesn't need actual model inference

Use `model` (real model) when:

Testing end-to-end functionality
Testing actual caption generation quality
Testing actual question answering
Integration tests that verify model behavior
Any test marked with @pytest.mark.long

Example Tests Added

The following new tests use the mock model:

test_image_summary_detector_init_mock - Tests initialization
test_load_pil_if_needed_string - Tests image loading
test_is_sequence_but_not_str - Tests utility methods
test_validate_analysis_type - Tests validation logic

All of these run quickly without loading the model.

Running Tests

Run only fast tests (with mocks):

pytest ammico/test/test_image_summary.py -v

Run only long tests (with real model):

pytest ammico/test/test_image_summary.py -m long -v

Run all tests:

pytest ammico/test/test_image_summary.py -v

Customizing the Mock

If you need to customize the mock's behavior for specific tests, you can override its methods:

def test_custom_behavior(mock_model):
    # Customize the mock's return value
    mock_model.tokenizer.batch_decode.return_value = ["custom", "output"]
    
    detector = ImageSummaryDetector(summary_model=mock_model, subdict={})
    # Test with custom behavior
    pass

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