Fix QLinearConv/PRelu edge cases and add NMS argmax option

[PINTO0309]

Summary

This PR improves robustness in quantized conversion paths and adds a new NonMaxSuppression option for class-score shrinking.

It addresses three concrete conversion failures seen during real model conversion, and adds a user-facing CLI/API switch for NMS behavior.

Background / Motivation

While converting quantized and post-process-heavy models, the following failure patterns were observed:

1. QLinearConv aborted when output shape metadata was None in the auto_pad == 'NOTSET' path.
2. QLinearConv depthwise weight reshape failed with mismatched element counts (invalid reshape target).
3. PRelu failed broadcasting when slope was channel-first style (e.g. [C,1,1]) but runtime tensor layout was channel-last.

In addition, for some NMS post-processing models (e.g. DAMO-YOLO style layouts), users requested a mode to shrink scores class dimension by argmax before NMS.

Changes

1) QLinearConv robustness fixes

File: onnx2tf/ops/QLinearConv.py

• Guarded SAME-padding shape comparison against missing metadata:
  • Before: accessed output_tensor_shape[2:] directly
  • After: checks both input/output shapes are not None before comparison
• Fixed depthwise filter reshape target:
  • Before: [..., input_weights_shape[2], input_weights_shape[3] // group]
  • After: [..., -1, input_weights_shape[3] // group]
  • This aligns behavior with Conv.py and avoids invalid reshape size errors.

2) PRelu slope layout alignment

File: onnx2tf/ops/PRelu.py

• Reworked slope handling to align slope layout with runtime input tensor layout.
• Added reshape logic for patterns like:
  • input: [N,H,W,C]
  • slope: [C,1,1]
  • transformed slope: [1,1,C]
• Implemented in a layout-agnostic way based on runtime tensor shape inspection.

3) New NMS option: --output_nms_with_argmax (-onwa)

Files:

• onnx2tf/onnx2tf.py
• onnx2tf/ops/NonMaxSuppression.py
• README.md

Added new CLI/API option to shrink class dimension of NMS scores:

• Input scores: [B, C, N]
• With option: argmax/reduce_max over class axis -> [B, 1, N]

Implementation details in NonMaxSuppression.py:

• Compute per-box class ids with argmax(scores, axis=1)
• Use reduced scores for NMS (reduce_max(..., keepdims=True))
• Reconstruct output [batch_index, class_index, box_index] by gathering class ids for selected boxes
• If scores rank is not 3, warn and fallback to existing behavior

4) Documentation and version update

Files:

• README.md

• onnx2tf/__init__.py

• pyproject.toml

• Documented the new NMS argmax option in README (CLI and Python API sections).

• Updated version from 2.0.8 to 2.0.9.

Validation

• python -m py_compile onnx2tf/onnx2tf.py onnx2tf/ops/QLinearConv.py onnx2tf/ops/PRelu.py onnx2tf/ops/NonMaxSuppression.py onnx2tf/__init__.py
• Sanity-checked NMS output tuple construction for argmax mode and verified output format [batch, class, box].

Compatibility / Risk

• Default behavior is unchanged unless --output_nms_with_argmax is explicitly enabled.
• QLinearConv and PRelu changes are defensive and targeted at previously failing shape/layout edge cases.

Issue

• [TODO] Implementation of the ArgMax force-replacement option for NMS with associated class score refinement. #420