[Official] ONNX Export Support with Dynamic Shapes
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 20/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Domain
- computer-vision, machine-learning
Research direction
Start by reading the prior discussion in #20 and the examples of dynamic-shape and UV-encoding export problems described here. The issue does not name files, tests, or a specific entry point; clarify the target export cases and acceptance criteria before implementation. Done should mean dynamic-shape exports work without bloated constant tensors.
Written by the indexing model from the issue text.
Description
As previously discussed in #20, many users have encountered challenges when exporting models to ONNX, particularly when handling dynamic input shapes. The main difficulties include:
-
Untraceable dynamic shape induction: ONNX export requires all dynamic reshaping operations to be driven by explicit shape tensors passed as inputs. This is often non-trivial to restructure.
-
On-the-fly UV encoding: Some modules generate UV encodings based on the input aspect ratio and spatial dimensions. These often introduce large constant tensors into the exported graph, which bloats the model and complicates correct export.
We are working on resolving these issues.
Feel free to leave a comment here if you’ve run into similar problems or have ideas on how to improve the export process.
- Dominant language
- JavaScript
- Stars
- 3k
- Forks
- 234
- PR merge metrics
- No merged PRs in 30d
Getting set up
We have not checked this project's setup files yet. Start from its README, and see our first-contribution guide for the general steps.
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.
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