a5b01ec04e
Add type annotations to _registry.py
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Description
Add type annotations to _registry.py so that they will pass mypy
--strict.
Comment
I was reading the code and felt that this module would be easier to
understand with type annotations. Therefore, I went ahead and added the
annotations.
The idea with this PR is to start small to see if we can align on _how_
to annotate types. I've seen people in the past disagree on how strictly
to annotate the code base, so before spending too much time on this, I
wanted to check if you agree, Ross.
Most of the added types should be straightforward. Some notes on the
non-trivial changes:
- I made no assumption about the fn passed to register_model, but maybe
the type could be stricter. Are all models nn.Modules?
- If I'm not mistaken, the type hint for get_arch_name was incorrect
- I had to add a # type: ignore to model.__all__ = ...
- I made some minor code changes to list_models to facilitate the
typing. I think the changes should not affect the logic of the function.
- I removed list from list(sorted(...)) because sorted returns always a
list.
2 years ago
47f1de9bec
Version bump
2 years ago
4d9c3ae2fb
Add laion2b 320x320 ConvNeXt-Large CLIP weights
2 years ago
d0b45c9b4d
Make safetensor import option for now. Improve avg/clean checkpoints ext handling a bit (more consistent).
2 years ago
7d9e321b76
Improve tracing of window attn models with simpler reshape logic
2 years ago
2e38d53dca
Remove dead line
2 years ago
f77c04ff36
Torchscript fixes/hacks for rms_norm, refactor ParallelScalingBlock with manual combination of input projections, closer paper match
2 years ago
122621daef
Add Final annotation to attn_fas to avoid symbol lookup of new scaled_dot_product_attn fn on old PyTorch in jit
2 years ago
621e1b2182
Add ideas from 'Scaling ViT to 22-B Params', testing PyTorch 2.0 fused F.scaled_dot_product_attention impl in vit, vit_relpos, maxxvit / coatnet.
2 years ago
a3d528524a
Version 0.8.12dev0
2 years ago
a09d403c24
changed warning to info
2 years ago
8470e29541
Add support to load safetensors weights
2 years ago
f35d6ea57b
Add multi-tensor (foreach) version of Lion in style of upcoming PyTorch 2.0 optimizers
2 years ago
709d5e0d9d
Add Lion optimizer
2 years ago
624266148d
Remove unused imports from _hub helpers
2 years ago
2cfff0581b
Add grad_checkpointing support to features_only, test in EfficientDet.
2 years ago
45af496197
Version 0.8.11dev0
2 years ago
9c14654a0d
Improve support for custom dataset label name/description through HF hub export, via pretrained_cfg
2 years ago
497be8343c
Update README and version
2 years ago
0d33127df2
Add 384x384 convnext_large_mlp laion2b fine-tune on in1k
2 years ago
7a0bd095cb
Update model prune loader to use pkgutil
2 years ago
0f2803de7a
Move ImageNet metadata (aka info) files to timm/data/_info. Add helper classes to make info available for labelling. Update inference.py for first use.
2 years ago
7a13be67a5
Update version.py
2 years ago
13acac8c5e
Update head metadata for effformerv2
2 years ago
8682528096
Add first conv metadata for efficientformer_v2
2 years ago
72fba669a8
is_scripting() guard on checkpoint_seq
2 years ago
95ec255f7f
Finish timm mode api for efficientformer_v2, add grad checkpointing support to both efficientformers
2 years ago
9d03c6f526
Merge remote-tracking branch 'origin/main' into levit_efficientformer_redux
2 years ago
086bd55a94
Add EfficientFormer-V2, refactor EfficientFormer and Levit for more uniformity across the 3 related arch. Add features_out support to levit conv models and efficientformer_v2. All weights on hub.
2 years ago
2cb2699dc8
Apply fix from #1649 to main
2 years ago
b3042081b4
Add laion -> in1k fine-tuned base and large_mlp weights for convnext
2 years ago
316bdf8955
Add mlp head support for convnext_large, add laion2b CLIP weights, prep fine-tuned weight tags
2 years ago
6f28b562c6
Factor NormMlpClassifierHead from MaxxViT and use across MaxxViT / ConvNeXt / DaViT, refactor some type hints & comments
2 years ago
9a53c3f727
Finalize DaViT, some formatting and modelling simplifications (separate PatchEmbed to Stem + Downsample, weights on HF hub.
2 years ago
fb717056da
Merge remote-tracking branch 'upstream/main'
2 years ago
2bbc26dd82
version 0.8.8dev0
2 years ago
64667bfa0e
Add 'gigantic' vit clip variant for feature extraction and future fine-tuning
2 years ago
c2822568ec
Update version to 0.8.7dev0
2 years ago
36989cfae4
Factor out readme generation in hub helper, add more readme fields
2 years ago
32f252381d
Change order of checkpoitn filtering fn application in builder, try dict, model variant first
2 years ago
e9f1376cde
Cleanup resolve data config fns, add 'model' variant that takes model as first arg, make 'args' arg optional in original fn
2 years ago
bed350f5e5
Push all MaxxViT weights to HF hub, cleanup impl, add feature map extraction support and prompote to 'std' architecture. Fix norm head for proper embedding / feat map output. Add new in12k + ft 1k weights.
2 years ago
ca38e1e73f
Update ClassifierHead module, add reset() method, update in_chs -> in_features for consistency
2 years ago
8ab573cd26
Add convnext_tiny and convnext_small 384x384 fine-tunes of in12k weights, fix pool size for laion CLIP convnext weights
2 years ago
81ca323751
Davit update formatting and fix grad checkpointing ( #7 )
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fixed head to gap->norm->fc as per convnext, along with option for norm->gap->fc
failed tests due to clip convnext models, davit tests passed
2 years ago
e9aac412de
Correct mean/std for CLIP convnexts
2 years ago
42bd8f7bcb
Add convnext_base CLIP image tower weights for fine-tuning / features
2 years ago
e520553e3e
Update batchnorm freezing to handle NormAct variants, Add GroupNorm1Act, update BatchNormAct2d tracing change from PyTorch
2 years ago
a2c14c2064
Add tiny/small in12k pretrained and fine-tuned ConvNeXt models
2 years ago
01aea8c1bf
Version 0.8.6dev0
2 years ago
Benjamin Bossan
Ross Wightman
testbot
Ross Wightman
Fredo Guan