2.2.0

2.2.0

Summary

This PR consolidates the fix-rtmdet branch improvements focused on functional reliability, direct FlatBuffer export robustness, and conversion/typing stability across the ONNX->TFLite pipeline.

Compared to main, this branch contains 3 commits and broad updates across operator converters, the flatbuffer_direct backend, tests, and tooling.

Functional Improvements (Feature-level)

1. Stronger flatbuffer_direct lowering correctness

• Improved dynamic-shape handling for shape-sensitive graph patterns (notably in reshape/flatten/pad/reduce paths).
• Fixed edge cases where lowering metadata and runtime shape tensors could diverge.
• Tightened reduce-axis behavior so non-constant axes are rejected early in dispatch validation when required by flatbuffer_direct semantics.
• Stabilized dynamic PAD lowering flow by normalizing pads inputs through deterministic INT32 casting before reshape/transpose composition.

Impact:

• More predictable lowering behavior for dynamic ONNX graphs.
• Fewer latent runtime mismatches between static IR metadata and runtime tensor semantics.

2. Expanded and hardened operator conversion logic

• Updated many ONNX op converters in onnx2tf/ops/* to improve type/shape safety and runtime consistency.
• Replaced fragile tensor indexing/operator assumptions in multiple places with safer conversion patterns for dynamic tensors.
• Strengthened handling of optional values, dynamic dtypes, and tensor-vs-scalar branches.

Impact:

• Improved conversion success rate on real-world models (including RTMDet-related conversion paths).
• Reduced failure modes caused by ambiguous shape/dtype inference.

3. flatbuffer_direct coverage and validation alignment

• Aligned op coverage reporting and validation outcomes with actual builder constraints.
• Ensured unsupported reasons are surfaced consistently (for example, requires_constant_input for dynamic reduce axes where applicable).

Impact:

• Coverage reports now better reflect true exportability constraints.
• Debugging unsupported nodes is more actionable.

4. Benchmarking and developer tooling improvements

• Added benchmark_tflite.py for CPU/CUDA-oriented TFLite latency measurement workflows.
• Minor CLI/package/docs alignment updates (onnx2tf.py, __init__.py, README.md, etc.).

Impact:

• Easier performance characterization for converted artifacts.
• Better operational feedback loop when iterating on model conversion quality/performance.

Test and Quality Improvements

Added/updated direct-backend tests

• Extended tests/test_tflite_builder_direct.py with additional scenarios around dynamic pad/flatten/reshape behavior and related shape-signature expectations.

Validation status

Executed targeted flatbuffer_direct suites after fixes:

python -m pytest -q \
  tests/test_tflite_builder_direct.py \
  tests/test_tflite_builder_preprocess.py \
  tests/test_tflite_builder_op_coverage.py \
  tests/test_tflite_builder_gridsample_validation.py \
  tests/test_flatbuffer_direct_op_error_report.py \
  tests/test_tflite_split_planner.py

Result:

• 554 passed, 1 warning

Scope of Changes

• Large converter hardening across onnx2tf/ops/*.
• Core flatbuffer_direct updates in:
  • onnx2tf/tflite_builder/op_builders/shape.py
  • onnx2tf/tflite_builder/op_registry.py
  • additional related builder/utility modules.
• Benchmark tooling and docs updates.

Why this matters for fix-rtmdet

RTMDet export paths heavily depend on stable handling of dynamic tensor metadata, reshape/pad/reduce behavior, and consistent converter typing assumptions. This branch improves those exact stability points, reducing conversion-time ambiguity and increasing confidence in direct TFLite generation paths.

• Unable to convert RTMDet-ins to tflite #761

What's Changed

• Improve flatbuffer_direct reliability and converter robustness for RTMDet workflows by @PINTO0309 in #897

Full Changelog: 2.1.5...2.2.0