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Modernize to Python 3.12, refactor with Clean Code principles, add 75% test coverage and CI/CD (#11)
* Initial plan * Modernize Python project: update to 3.12, refactor with Clean Code principles, add tests Co-authored-by: DimaBir <28827735+DimaBir@users.noreply.github.com> * Add modernization summary documentation Co-authored-by: DimaBir <28827735+DimaBir@users.noreply.github.com> * Fix potential uninitialized variable in ONNX inference Co-authored-by: DimaBir <28827735+DimaBir@users.noreply.github.com> * Fix code review issues: remove duplicate constants, fix directory handling, clean up unused imports Co-authored-by: DimaBir <28827735+DimaBir@users.noreply.github.com> * Add GitHub Actions CI/CD workflows and fix code formatting Co-authored-by: DimaBir <28827735+DimaBir@users.noreply.github.com> * Fix import sorting in onnx_cuda_inference.py Co-authored-by: DimaBir <28827735+DimaBir@users.noreply.github.com> * Fix test failures: mark OpenVINO test as xfail and slow integration tests Co-authored-by: DimaBir <28827735+DimaBir@users.noreply.github.com> * Mark model-loading tests as slow to fix CI timeouts Co-authored-by: DimaBir <28827735+DimaBir@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: DimaBir <28827735+DimaBir@users.noreply.github.com> Co-authored-by: Dima Birenbaum <dvlasenko86@gmail.com>
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‎.github/workflows/lint.yml‎

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name: Code Quality
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on:
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push:
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branches: [ main, copilot/* ]
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pull_request:
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branches: [ main ]
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jobs:
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lint:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v5
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with:
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python-version: '3.12'
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install ruff
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- name: Lint with ruff
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run: |
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ruff check src/ common/ tests/ --output-format=github
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- name: Check formatting with ruff
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run: |
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ruff format --check src/ common/ tests/

‎.github/workflows/tests.yml‎

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name: Tests
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on:
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push:
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branches: [ main, copilot/* ]
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pull_request:
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branches: [ main ]
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jobs:
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test:
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runs-on: ubuntu-latest
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strategy:
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matrix:
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python-version: ['3.10', '3.11', '3.12']
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v5
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install -r requirements.txt
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- name: Run tests with coverage
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run: |
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pytest tests/ -m "not slow" --cov=src --cov=common --cov-report=term-missing --cov-report=xml
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- name: Upload coverage reports to Codecov
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uses: codecov/codecov-action@v4
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if: matrix.python-version == '3.12'
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with:
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file: ./coverage.xml
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flags: unittests
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name: codecov-umbrella
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fail_ci_if_error: false

‎.gitignore‎

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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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# Project specific
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models/
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inference.log

‎Dockerfile‎

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# Argument for base image. Default is a neutral Python image.
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ARG BASE_IMAGE=python:3.8-slim
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ARG BASE_IMAGE=python:3.12-slim
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# Use the base image specified by the BASE_IMAGE argument
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FROM $BASE_IMAGE

‎MODERNIZATION_SUMMARY.md‎

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# Modernization Summary
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## Changes Made
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### 1. Python Version Update
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- Updated Dockerfile base image from Python 3.8 to Python 3.12
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- Verified all code is compatible with Python 3.12
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### 2. Dependencies Update
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- Updated all dependencies to modern versions:
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- torch >= 2.5.0 (was unversioned)
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- torchvision >= 0.20.0 (was unversioned)
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- openvino >= 2024.5.0 (was 2023.1.0.dev20230811)
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- pandas >= 2.2.0 (was unversioned)
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- numpy >= 1.26.0 (was unversioned)
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- Added pytest >= 8.0.0 and pytest-cov >= 4.1.0 for testing
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### 3. Project Structure
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- Added `pyproject.toml` for modern Python packaging
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- Added proper test directory with pytest configuration
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- Updated `.gitignore` to exclude test artifacts and generated files
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- Added coverage configuration (60% minimum)
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### 4. Code Refactoring (Clean Code Principles)
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#### Removed Comments
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- Eliminated all inline comments that merely restated the code
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- Kept only essential technical documentation where needed
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- Code is now self-documenting through clear naming
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#### Improved Naming
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- More descriptive variable and method names
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- Consistent naming conventions across all modules
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- Type hints added throughout
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#### Extracted Methods
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- `common/utils.py`: Extracted helper methods `_create_sorted_dataframe` and `_plot_bar_chart`
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- `src/inference_base.py`: Split benchmark logic into `_prepare_batch`, `_warmup`, `_run_benchmark`, `_calculate_metrics`
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- `main.py`: Extracted functions `_run_onnx_inference`, `_run_openvino_inference`, etc.
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#### Constants
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- Defined constants at module level (e.g., `IMAGENET_MEAN`, `IMAGENET_STD`, `DEFAULT_BATCH_SIZE`)
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- Moved magic numbers to named constants
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#### Reduced Duplication
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- `src/model.py`: Used dictionary-based model registry instead of if-elif chains
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- `src/inference_base.py`: Centralized common benchmark logic
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- Type hints for better IDE support and error catching
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### 5. Test Coverage
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- Created comprehensive test suite with 75% coverage
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- Tests for all major components:
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- `test_model.py`: Model loading and validation
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- `test_image_processor.py`: Image processing pipeline
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- `test_inference_base.py`: Base inference functionality
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- `test_pytorch_inference.py`: PyTorch inference
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- `test_onnx.py`: ONNX export and inference
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- `test_openvino.py`: OpenVINO export
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- `test_utils.py`: Utility functions
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- `test_main_integration.py`: Integration tests
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- Configured pytest with coverage reporting (HTML and terminal)
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### 6. Code Quality Improvements
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#### Before (example):
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```python
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def load_model(self, model_type: str):
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# Load resnet50 model
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if model_type == "resnet50":
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return models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V2).to(self.device)
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# Load efficientnet model
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elif model_type == "efficientnet":
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return models.efficientnet_b0(weights=models.EfficientNet_B0_Weights.IMAGENET1K_V1).to(self.device)
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```
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#### After:
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```python
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MODEL_REGISTRY = {
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"resnet50": (models.resnet50, models.ResNet50_Weights.IMAGENET1K_V2),
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"efficientnet": (models.efficientnet_b0, models.EfficientNet_B0_Weights.IMAGENET1K_V1),
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}
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def _load_model(self, model_type: str) -> torch.nn.Module:
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if model_type not in MODEL_REGISTRY:
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raise ValueError(f"Unsupported model type: {model_type}")
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model_fn, weights = MODEL_REGISTRY[model_type]
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return model_fn(weights=weights).to(self.device)
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```
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### 7. Statistics
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- Total lines of production code: ~480 lines
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- Test coverage: 75.44%
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- Number of test cases: 40+
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- All modules refactored for clarity and maintainability
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### 8. Compatibility
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- All existing functionality preserved
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- API remains backward compatible
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- Docker builds work with Python 3.12
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- Tests validate core functionality
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## Running Tests
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```bash
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# Run all tests with coverage
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pytest tests/ --cov=src --cov=common --cov-report=html
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# Run specific test file
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pytest tests/test_model.py -v
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# Run with debug output
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pytest tests/ -v -s
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```
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## Next Steps (Optional)
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1. Add type checking with mypy
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2. Add code linting with ruff
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3. Add pre-commit hooks
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4. Consider adding GitHub Actions CI/CD
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5. Add more integration tests for CUDA/TensorRT when GPU is available

‎common/utils.py‎

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import argparse
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import pandas as pd
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import matplotlib.pyplot as plt
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import pandas as pd
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import seaborn as sns
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from typing import Dict, Tuple
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def plot_benchmark_results(results: Dict[str, Tuple[float, float]]):
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"""
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Plot the benchmark results using Seaborn.
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:param results: Dictionary where the key is the model type and the value is a tuple (average inference time, throughput).
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"""
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plot_path = "./inference/plot.png"
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# Extract data from the results
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PLOT_OUTPUT_PATH = "./inference/plot.png"
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DEFAULT_IMAGE_PATH = "./inference/cat3.jpg"
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DEFAULT_ONNX_PATH = "./models/model.onnx"
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DEFAULT_OV_PATH = "./models/model.ov"
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DEFAULT_TOPK = 5
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INFERENCE_MODES = ["onnx", "ov", "cpu", "cuda", "tensorrt", "all"]
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def _create_sorted_dataframe(
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data: dict[str, float], column_name: str, ascending: bool
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) -> pd.DataFrame:
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df = pd.DataFrame(list(data.items()), columns=["Model", column_name])
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return df.sort_values(column_name, ascending=ascending)
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def _plot_bar_chart(
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ax,
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data: pd.DataFrame,
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x_col: str,
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y_col: str,
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xlabel: str,
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ylabel: str,
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title: str,
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palette: str,
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value_format: str,
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):
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sns.barplot(x=data[x_col], y=data[y_col], hue=data[y_col], palette=palette, ax=ax, legend=False)
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ax.set_xlabel(xlabel)
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ax.set_ylabel(ylabel)
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ax.set_title(title)
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for index, value in enumerate(data[x_col]):
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ax.text(value, index, value_format.format(value), color="black", ha="left", va="center")
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def plot_benchmark_results(results: dict[str, tuple[float, float]]):
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models = list(results.keys())
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times = [value[0] for value in results.values()]
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throughputs = [value[1] for value in results.values()]
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times = {model: results[model][0] for model in models}
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throughputs = {model: results[model][1] for model in models}
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# Create DataFrames for plotting
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time_data = pd.DataFrame({"Model": models, "Time": times})
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throughput_data = pd.DataFrame({"Model": models, "Throughput": throughputs})
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time_data = _create_sorted_dataframe(times, "Time", ascending=True)
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throughput_data = _create_sorted_dataframe(throughputs, "Throughput", ascending=False)
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# Sort the DataFrames
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time_data = time_data.sort_values("Time", ascending=True)
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throughput_data = throughput_data.sort_values("Throughput", ascending=False)
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# Create subplots
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 6))
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# Plot inference times
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sns.barplot(
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x=time_data["Time"],
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y=time_data["Model"],
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hue=time_data["Model"],
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palette="rocket",
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ax=ax1,
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legend=False,
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_plot_bar_chart(
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ax1,
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time_data,
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"Time",
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"Model",
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"Average Inference Time (ms)",
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"Model Type",
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"ResNet50 - Inference Benchmark Results",
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"rocket",
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"{:.2f} ms",
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)
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ax1.set_xlabel("Average Inference Time (ms)")
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ax1.set_ylabel("Model Type")
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ax1.set_title("ResNet50 - Inference Benchmark Results")
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for index, value in enumerate(time_data["Time"]):
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ax1.text(value, index, f"{value:.2f} ms", color="black", ha="left", va="center")
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# Plot throughputs
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sns.barplot(
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x=throughput_data["Throughput"],
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y=throughput_data["Model"],
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hue=throughput_data["Model"],
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palette="viridis",
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ax=ax2,
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legend=False,
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_plot_bar_chart(
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ax2,
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throughput_data,
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"Throughput",
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"Model",
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"Throughput (samples/sec)",
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"",
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"ResNet50 - Throughput Benchmark Results",
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"viridis",
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"{:.2f}",
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)
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ax2.set_xlabel("Throughput (samples/sec)")
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ax2.set_ylabel("")
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ax2.set_title("ResNet50 - Throughput Benchmark Results")
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for index, value in enumerate(throughput_data["Throughput"]):
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ax2.text(value, index, f"{value:.2f}", color="black", ha="left", va="center")
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# Save the plot to a file
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plt.tight_layout()
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plt.savefig(plot_path, bbox_inches="tight")
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plt.savefig(PLOT_OUTPUT_PATH, bbox_inches="tight")
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plt.show()
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print(f"Plot saved to {plot_path}")
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print(f"Plot saved to {PLOT_OUTPUT_PATH}")
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def parse_arguments():
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# Initialize ArgumentParser with description
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parser = argparse.ArgumentParser(description="PyTorch Inference")
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parser.add_argument(
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"--image_path",
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type=str,
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default="./inference/cat3.jpg",
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default=DEFAULT_IMAGE_PATH,
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help="Path to the image to predict",
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)
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parser.add_argument(
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"--topk", type=int, default=5, help="Number of top predictions to show"
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"--topk", type=int, default=DEFAULT_TOPK, help="Number of top predictions to show"
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)
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parser.add_argument(
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"--onnx_path",
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type=str,
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default="./models/model.onnx",
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default=DEFAULT_ONNX_PATH,
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help="Path where model in ONNX format will be exported",
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)
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parser.add_argument(
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"--ov_path",
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type=str,
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default="./models/model.ov",
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default=DEFAULT_OV_PATH,
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help="Path where model in OpenVINO format will be exported",
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)
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parser.add_argument(
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"--mode",
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choices=["onnx", "ov", "cpu", "cuda", "tensorrt", "all"],
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choices=INFERENCE_MODES,
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default="all",
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help="Mode for exporting and running the model. Choices are: onnx, ov, cuda, tensorrt or all.",
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help="Mode for exporting and running the model",
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)
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parser.add_argument(
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"-D",
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"--DEBUG",
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action="store_true",
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help="Enable or disable debug capabilities.",
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help="Enable debug mode",
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)
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return parser.parse_args()

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