tensorflow / tensorflow/model-optimization
Support for custom layers composed of standard keras layers
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Description
I'm wondering how I can create a quantization configuration for a custom layer (which implements the keras layer class) where my custom layer is composed of other standard keras layers (such as Conv2D, BatchNorm etc.)
Assuming that every sub-layer in my custom one is either supported or can be skipped for quantization, can I supply a quantization config that just recursively checks the existing default8bitquantizeregistry or something like that?
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the custom Keras layer quantization configuration and the existing default8bitquantizeregistry behavior mentioned in the issue. Determine whether supported or skipped sub-layers can be traversed recursively, including composed Conv2D and BatchNorm layers. Done should define and validate how quantization configuration works for these custom layers.
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Assessment
- Tech stack
- keras, python, tensorflow
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100