The convert_weights method in rtmcc_head.py checks if fl_w in state_dict before applying the conversion, but then unconditionally accesses state_dict[fl_b] without a corresponding guard. If a checkpoint uses a bias-free final_layer, this will raise a KeyError rather than silently skipping — worth adding a separate if fl_b in state_dict check or bundling both under a single check that verifies both keys exist.

The fallback torch.rand((), ...) when old_vals is empty introduces non-determinism into weight loading, which is particularly problematic for reproducibility of fine-tuning runs. Since this branch handles positions where no old keypoint pair contributes (an edge case whose practical frequency is unclear), torch.zeros or w_old.mean() would be safer and deterministic defaults.

It's also worth documenting the assumption baked into old_idx = int(conv[i] - conv[j]) + k_old - 1: this treats differences in original keypoint indices as relative positional distances in the GAU weight table. That coupling between semantic keypoint ordering and positional encoding is non-obvious and could silently produce incorrect weight remappings if the pretrained model was trained with a different keypoint ordering convention.

Pull request overview

This PR fixes SuperAnimal pretrained checkpoint loading for RTMPose models by adding weight-conversion support to RTMCCHead, aligning it with other heads that can subset/reorder keypoints during transfer learning.

Changes:

• Make RTMCCHead subclass WeightConversionMixin to participate in the SuperAnimal conversion workflow.
• Implement RTMCCHead.convert_weights(...) to remap final_layer output channels and adapt gau.w to new keypoint counts.

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