feat: add ONNX Runtime inference backend with PyTorch fallback (#12)

[jshaofa-ui]

[Good First Issue] Add ONNX Runtime inference path with PyTorch fallback

Resolves #12

Summary

Implements a complete ONNX Runtime inference backend for ClimateVision with automatic PyTorch fallback, enabling faster inference on CPU and edge devices while maintaining full compatibility with existing PyTorch models.

Changes

• onnx_runtime.py (540 lines) - ONNX Runtime engine with session management, inference, and benchmarking
• onnx_export.py (422 lines) - PyTorch to ONNX model export for U-Net and Siamese networks
• init.py (67 lines) - Unified module API combining PyTorch and ONNX inference
• test_onnx_runtime.py (873 lines) - 32 unit tests across 11 test classes
• onnx-runtime-guide.md - Complete usage documentation

Core Features

1. ONNXSession - Cached session manager with automatic CPU/CUDA provider selection
2. run_onnx_inference() - Batch inference with latency tracking
3. benchmark_onnx_model() - Full benchmarking (p50/p95/p99 latency + FPS)
4. export_unet_to_onnx() / export_siamese_to_onnx() - Dynamic axis, configurable opset
5. run_inference_with_fallback() - Automatic ONNX to PyTorch fallback
6. validate_onnx_model() - Cross-validation with PyTorch output

Test Coverage

• 11 test classes: ONNXSession caching, device selection, inference, benchmarking, export, validation, fallback, integration
• 32 unit tests total
• Graceful skip when torch/onnx not available

Technical Details

• Zero breaking changes to existing inference pipeline
• Automatic provider selection based on hardware availability
• Session caching for repeated inference calls
• Full numerical validation against PyTorch baseline