weight-conversion

name	Weight Conversion
description	Converting PyTorch model weights to Keras h5 format for keras_cv_attention_models

Weight Conversion Skill

Convert pretrained PyTorch weights to Keras h5 format.

The core task is aligning the weight name order between torch and keras models. download_and_load.keras_reload_from_torch_model is a convenience helper that automates this, but direct manual conversion is also fine.

Pipeline Overview

1. Weight collection (state_dict_stack_by_layer): Groups torch state_dict entries by layer name (splitting on .), filtering via skip_weights and unstack_weights.
2. Name alignment (align_layer_names_multi_stage): Reorders keras layer names to match torch weight order.
3. Weight transfer (keras_reload_stacked_state_dict): Applies standard transforms (Conv2D/Dense transpose, etc.) plus custom additional_transfer overrides, then saves.

Parameter Reference

Weight Collection

Parameter	Purpose
skip_weights	Weight name suffixes to drop (e.g., ["num_batches_tracked", "relative_position_index"])
unstack_weights	Weights kept as individual entries instead of grouped with their layer (e.g., ["cls_token", "pos_embed", "gamma_1"])

Name Alignment (order matching)

Parameter	Purpose
tail_align_dict	Reposition layers by tail name: {tail_name: offset}. Negative offset moves layer earlier. Can be scoped by stack: {"stack3": {"attn_gamma": -6}}
full_name_align_dict	Reposition by exact name: value can be negative offset, absolute position, or another layer's name string
tail_split_position	Where to split name into head/tail (default 2). E.g., 1 → head=stack1, tail=attn_gamma
specific_match_func	Function returning the complete ordered name list, bypassing all alignment logic. Use for complex cases where dicts can't express the mapping

Weight Transfer

Parameter	Purpose
additional_transfer	Custom transforms: {LayerClass: lambda ww: [...]} or {"name_suffix": lambda ww: [...]}. Applied after default Conv2D/Dense transposes

Workflow

1. Create keras model with pretrained=None, classifier_activation=None
2. Run with do_convert=False first to inspect both name lists
3. Compare printed torch/keras weight lists — find misalignments
4. Configure alignment parameters to fix ordering
5. Run with do_convert=True — it predicts with both models and prints results
6. Verify top prediction matches (usually Egyptian_cat for the cat test image)
7. md5sum output.h5 → add hash to PRETRAINED_DICT
8. Upload to GitHub releases. Notify user if cannot upload directly.

Troubleshooting

• Shape mismatch: Dense/Conv transposes are automatic; check if combined QKV needs unstack_weights
• Name ordering wrong: Use do_convert=False to see lists side-by-side; adjust offsets or use specific_match_func for full control
• Predictions don't match: Check rescale_mode in add_pre_post_process(), classifier_activation, or intermediate layer outputs