Description
Dense prediction tasks such as semantic segmentation and object detection need normalized intermediate feature maps from multiple backbone layers or stages. Multi-scale features can also be useful during pretraining and distillation, for example to define objectives or transfer representations at more than one level of the network, e.g. wanted in #628 .
LightlyTrain already defines a public interface for this:
DINOv2 and DINOv3 provide reference implementations:
We would like to extend this interface to the remaining model packages, starting with TIMM. The current TIMMModelWrapper already uses TIMM's forward_intermediates where available, which should provide a useful starting point.
EdgeCrafter is partially supported: ECViTModelWrapper.forward() already returns a three-level feature pyramid, but its wrapper and package do not yet implement the public multi-scale protocols.
Contributions can address one package at a time; there is no need to implement the whole checklist in a single PR. If you would like to work on one, please leave a comment so work is not duplicated.
Expected behavior
For each package:
- Make the package implement
MultiScaleFeaturePackage.
- Make its wrapper implement
forward_multiscale_features() and the applicable metadata methods.
- Return normalized NCHW feature tensors in the same order as the requested layer or stage indices.
- Expose correct feature dimensions and, depending on the architecture, patch size or feature strides.
- Fail with a clear error for models that cannot expose intermediate features instead of returning incorrect results.
- Add tests for metadata, output ordering and shapes, invalid indices, and consistency with
forward_features() for the final layer or stage.
Package-specific implementations do not have to support every upstream architecture immediately. Clearly documented and tested support for a useful subset is welcome, provided unsupported models are handled explicitly.
Package checklist
Custom model wrappers are not included in this checklist because they are user-defined, but they can implement the same protocols.
Description
Dense prediction tasks such as semantic segmentation and object detection need normalized intermediate feature maps from multiple backbone layers or stages. Multi-scale features can also be useful during pretraining and distillation, for example to define objectives or transfer representations at more than one level of the network, e.g. wanted in #628 .
LightlyTrain already defines a public interface for this:
ForwardMultiScaleFeatures,MultiScaleFeatureDims, andMultiScaleFeatureStridesMultiScaleFeatureViTandMultiScaleFeatureCNNMultiScaleFeaturePackageDINOv2 and DINOv3 provide reference implementations:
We would like to extend this interface to the remaining model packages, starting with TIMM. The current
TIMMModelWrapperalready uses TIMM'sforward_intermediateswhere available, which should provide a useful starting point.EdgeCrafter is partially supported:
ECViTModelWrapper.forward()already returns a three-level feature pyramid, but its wrapper and package do not yet implement the public multi-scale protocols.Contributions can address one package at a time; there is no need to implement the whole checklist in a single PR. If you would like to work on one, please leave a comment so work is not duplicated.
Expected behavior
For each package:
MultiScaleFeaturePackage.forward_multiscale_features()and the applicable metadata methods.forward_features()for the final layer or stage.Package-specific implementations do not have to support every upstream architecture immediately. Clearly documented and tested support for a useful subset is welcome, provided unsupported models are handled explicitly.
Package checklist
Custom model wrappers are not included in this checklist because they are user-defined, but they can implement the same protocols.