Weight Conversion

Development

Converting PyTorch model weights to Keras h5 format for keras_cv_attention_models

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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

Weight collection (state_dict_stack_by_layer): Groups torch state_dict entries by layer name (splitting on .), filtering via skip_weights and unstack_weights.
Name alignment (align_layer_names_multi_stage): Reorders keras layer names to match torch weight order.
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

Create keras model with pretrained=None, classifier_activation=None
Run with do_convert=False first to inspect both name lists
Compare printed torch/keras weight lists — find misalignments
Configure alignment parameters to fix ordering
Run with do_convert=True — it predicts with both models and prints results
Verify top prediction matches (usually Egyptian_cat for the cat test image)
md5sum output.h5 → add hash to PRETRAINED_DICT
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