AgentSkillsCN

Weight Conversion

将 PyTorch 模型权重转换为 Keras h5 格式，以适配 keras_cv_attention_models。

SKILL.md
--- frontmatter
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

ParameterPurpose
skip_weightsWeight name suffixes to drop (e.g., ["num_batches_tracked", "relative_position_index"])
unstack_weightsWeights kept as individual entries instead of grouped with their layer (e.g., ["cls_token", "pos_embed", "gamma_1"])

Name Alignment (order matching)

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

Weight Transfer

ParameterPurpose
additional_transferCustom 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