6c2edf4d74
Missed hub_id entries for byoanet models
2023-05-10 15:58:55 -07:00
850ab4931f
Missed a few pretrained tags...
2023-05-10 12:16:30 -07:00
ff2464e2a0
Throw when pretrained weights not available and pretrained=True (principle of least surprise).
2023-05-10 10:44:34 -07:00
e0ec0f7252
Merge pull request #1643 from nateraw/docstrings-update
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Update Docstring for create_model
2023-05-09 21:33:20 -07:00
627b6315ba
Add typing to dinov2 entrypt fns, use hf hub for mae & dinov2 weights
2023-05-09 20:42:11 -07:00
960a882510
Remove label offsets and remove old weight url for 1001 class (background + in1k) TF origin weights
2023-05-09 18:00:41 -07:00
a01d8f86f4
Tweak DinoV2 add, add MAE ViT weights, add initial intermediate layer getter experiment
2023-05-09 17:59:22 -07:00
59bea4c306
Merge branch 'main' into dot_nine_cleanup
2023-05-09 12:27:32 -07:00
5cc87e6485
Add dinov2 pretrained models ( #1797 )
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* add dinov2 small, base, and large
* fix input size
* fix swiglu & dinov2 vit giant
* use SwiGLUPacked to replace GluMlp
* clean up & add ffn_layer placeholder for ParallelScalingBlock
2023-05-09 12:24:47 -07:00
21e57c0b9e
Add missing beitv2 in1k -> in1k models
2023-05-08 17:03:51 -07:00
8c6fccb879
Allow passing state_dict directly via pretrained cfg mechanism as an override
2023-05-08 15:15:44 -07:00
e4e43190ce
Add typing to all model entrypoint fns, add old cache check env var to builder
2023-05-08 08:52:38 -07:00
cb3f9c23bb
Metaformer baselines for vision (final PR with cleanup) ( #1793 )
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* update
* Update metaformers.py
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* merge with poolformer, initial version
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* Revert "Update metaformers.py"
This reverts commit 2916f37f8d1d882eb4942209d0830e32bede4e274ed934e0003f0b0753672fef9006d7b7696a30a741fe5c3626a3aee37c35f938beb81b10bde717e539274bd45ea2329ab8ec53b8ce5b8aab6225b94102fcc30eaa366aae930426a8e481a5a913f5d438https://github.com/fffffgggg54/pytorch-image-models into metaformer_workspace
commit d577129aaa23fb348a8bb93bcd17cf1d5a4e8ff8
Author: Fredo Guan <fredo.guan@hotmail.com>
Date: Mon Mar 6 01:57:20 2023 -0800
Update metaformers.py
commit f7367304e8f3b7a9a7f16e0a032bb72546afcc2a
Author: Fredo Guan <fredo.guan@hotmail.com>
Date: Mon Mar 6 01:56:11 2023 -0800
Metaformer baselines for vision (#12 )
* formatting, cleanup, fix dropout
* fix regression, pass kwargs
* fix poolformerv1 weights, formatting
* Update metaformers.py
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* some cleanup
* SDPA from ViT, fix imports
* Update metaformers.py
* fix head reset
* fast norm bias patch for metaformers
* Metaformer refactor, remove rand/ident models, fix issues, remove old poolformer
* Switch to hub weights
---------
Co-authored-by: Fredo Guan <fredo.guan@hotmail.com>
2023-05-05 11:18:26 -07:00
320bf9c469
Remove redundant types, kwargs back in own section (lesser of many evils?)
2023-05-01 14:21:48 -07:00
8fa86a28a8
Add datacomp L/14 (79.2 zs) image tower weights
2023-05-01 10:24:08 -07:00
5e64777804
0.8.21dev0
2023-04-28 13:46:59 -07:00
493c730ffc
Fix pit regression
2023-04-26 23:16:06 -07:00
437d344e03
Always some torchscript issues
2023-04-26 20:42:34 -07:00
528faa0e04
Some fixes
2023-04-26 17:46:20 -07:00
3386af8c86
Final push to get remaining models using multi-weight pretrained configs, almost all weights on HF hub
2023-04-26 15:52:13 -07:00
7ad7ddb7ad
DenseNet, DPN, VoVNet, Aligned Xception weights on HF hub. DenseNet grad_checkpointing using timm API
2023-04-21 16:56:44 -07:00
864bfd43d0
hardcore nas weights on hf hub
2023-04-21 14:35:10 -07:00
6e4529ae35
TResNet weights now on HF hub, modified to remove InplaceABN dependency
2023-04-21 14:20:48 -07:00
04dcbc02ec
Fix weight remap for tresnet_v2_l
2023-04-21 09:05:04 -07:00
a08e5aed1d
More models w/ multi-weight support, moving to HF hub. Removing inplace_abn from all models including TResNet
2023-04-20 22:44:49 -07:00
34df125be6
cait, volo, xvit hub weights
2023-04-14 10:13:13 -07:00
f6d5767551
cspnet models on HF hub w/ multi-weight support
2023-04-12 14:02:38 -07:00
80b247d843
Update swin_v2 attn_mask buffer change in #1790 to apply to updated checkpoints in hub
2023-04-11 14:40:32 -07:00
1a1aca0cee
Merge pull request #1761 from huggingface/patch_drop_refactor
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Implement patch dropout for eva / vision_transformer, refactor dropout args
2023-04-11 14:37:36 -07:00
c0670822d2
Small factory handling fix for pretrained tag vs cfg
2023-04-11 07:42:13 -07:00
2f25f73b90
Missed a fused_attn update in relpos vit
2023-04-10 23:30:50 -07:00
0b65b5c0ac
Add finalized eva CLIP weights pointing to remapped timm hub models
2023-04-10 23:13:12 -07:00
965d0a2d36
fast_attn -> fused_attn, implement global config for enable/disable fused_attn, add to more models. vit clip openai 336 weights.
2023-04-10 12:04:33 -07:00
4d135421a3
Implement patch dropout for eva / vision_transformer, refactor / improve consistency of dropout args across all vit based models
2023-04-07 20:27:23 -07:00
c76818a592
skip attention mask buffers
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Allows more flexibility in the resolutions accepted by SwinV2.
2023-04-07 18:50:02 +02:00
1bb3989b61
Improve kwarg passthrough for swin, vit, deit, beit, eva
2023-04-05 21:37:16 -07:00
35c94b836c
Update warning message for deprecated model names
2023-04-05 17:24:17 -07:00
9eaab795c2
Add some vit model deprecations
2023-04-05 17:21:03 -07:00
abff3f12ec
Wrong pool_size for 288 ft
2023-04-05 16:07:51 -07:00
356309959c
ResNet models on HF hub, multi-weight support, add torchvision v2 weights, new 12k pretrained and fine-tuned timm anti-aliased weights
2023-04-05 14:19:42 -07:00
beef7f0a22
Add ImageNet-12k intermediate fine-tunes of convnext base & large CLIP models, add first 1k fine-tune of xxlarge
2023-03-31 16:45:01 -07:00
9aa1133bd2
Fix #1750 , uncomment weight that exists on HF hub, add FIXME to 3 others that are still on local storage
2023-03-31 14:49:30 -07:00
7326470514
Merge pull request #1746 from huggingface/eva02
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Adding EVA02 weights and model defs
2023-03-31 12:17:00 -07:00
adeb9de7c6
Mismatch in eva pretrained_cfg vs model for one of the clip variants
2023-03-31 10:30:30 -07:00
0737bd3ec8
eva02 non-CLIP weights on HF hub, add initial eva02 clip model configs w/ postnorm variant & attn LN
2023-03-30 23:43:59 -07:00
ac67098147
Add final attr for fast_attn on beit / eva
2023-03-28 08:40:40 -07:00
1885bdc431
Merge pull request #1745 from huggingface/mw-mlp_mixer
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MLP-Mixer multi-weight support, HF hub push
2023-03-28 07:55:17 -07:00
e9f427b953
Add hf hub entries for mlp_mixer
2023-03-27 22:50:43 -07:00
cff81deb78
multi-weight and hf hub for deit / deit3
2023-03-27 22:47:16 -07:00
3863d63516
Adding EVA02 weights and model defs, move beit based eva_giant to same eva.py file. Cleanup rotary pos, add lang oriented freq bands to be compat with eva design choice. Fix #1738
2023-03-27 17:16:07 -07:00
b12060996c
MLP-Mixer multi-weight support, hf hub push
2023-03-27 16:42:13 -07:00
d196fa536d
Fix last min torchscript regression in nfnet changes
2023-03-24 00:10:17 -07:00
33ada0cbca
Add group_matcher to focalnet for proper layer-wise LR decay
2023-03-23 23:21:49 -07:00
b271dc0e16
NFNet multi-weight support + HF hub push
2023-03-23 23:20:38 -07:00
dbd33e4b62
Update crop settings for new rexnet weights
2023-03-22 15:39:49 -07:00
da6bdd4560
Update resnetv2.py for multi-weight and HF hub weights
2023-03-22 15:38:04 -07:00
b3e816d6d7
Improve filtering behaviour for tag + non-tagged model wildcard consistency.
2023-03-22 10:21:22 -07:00
7aba64ebdb
Add update byobnet.py w/ models pushed to HF hub
2023-03-22 10:00:00 -07:00
e7ef8335bf
regnet.py multi-weight conversion, new ImageNet-12k pretrain/ft from timm for y_120 and y_160, also new tv v2, swag, & seer weights for push to Hf hub.
2023-03-21 15:51:49 -07:00
c78319adce
Add ImageNet-12k ReXNet-R 200 & 300 weights, and push existing ReXNet models to HF hub. Dilation support added to rexnet
2023-03-20 13:48:17 -07:00
041de79f9e
Fix numel use in helpers for checkpoint remap
2023-03-20 09:36:48 -07:00
49b9c3be80
Include pretrained tag in deprecated mapping warning
2023-03-19 21:21:19 -07:00
572f05096a
Swin and FocalNet weights on HF hub. Add model deprecation functionality w/ some registry tweaks.
2023-03-18 14:55:09 -07:00
5aebad3fbc
return_map back to out_map for _feature helpers
2023-03-16 14:50:55 -07:00
acfd85ad68
All swin models support spatial output, add output_fmt to v1/v2 and use ClassifierHead.
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* update ClassifierHead to allow different input format
* add output format support to patch embed
* fix some flatten issues for a few conv head models
* add Format enum and helpers for tensor format (layout) choices
2023-03-15 23:21:51 -07:00
c30a160d3e
Merge remote-tracking branch 'origin/main' into focalnet_and_swin_refactor
2023-03-15 15:58:39 -07:00
ad94d737b7
Add support to ConvNextBlock for downsample and channel expansion to improve stand alone use. Fix #1699
2023-03-13 14:06:24 -07:00
992bf7c3d4
chore: Modify the MobileVitV2Block to be coreml exportable
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based on is_exportable() set variable controlling behaviour of the block
CoreMLTools support im2col from 6.2 version, unfortunately col2im
is still not supported.
Tested with exporting to ONNX, Torchscript, CoreML, and TVM.
2023-03-03 09:38:24 +01:00
4b8cfa6c0a
Add convnext_xxlarge CLIP image tower weights, version 0.8.15dev0
2023-02-26 21:51:48 -08:00
1c13ef7b46
Add default norm_eps=1e-5 for convnext_xxlarge, improve kwarg merging for all convnext models
2023-02-26 12:11:49 -08:00
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.
2023-02-22 09:19:30 -08:00
4d9c3ae2fb
Add laion2b 320x320 ConvNeXt-Large CLIP weights
2023-02-18 16:34:03 -08:00
d0b45c9b4d
Make safetensor import option for now. Improve avg/clean checkpoints ext handling a bit (more consistent).
2023-02-18 16:06:42 -08:00
947c1d757a
Merge branch 'main' into focalnet_and_swin_refactor
2023-02-17 16:28:52 -08:00
cf324ea38f
Fix grad checkpointing in focalnet
2023-02-17 16:26:26 -08:00
848d200767
Overhaul FocalNet implementation
2023-02-17 16:24:59 -08:00
7266c5c716
Merge branch 'main' into focalnet_and_swin_refactor
2023-02-17 09:20:14 -08:00
7d9e321b76
Improve tracing of window attn models with simpler reshape logic
2023-02-17 07:59:06 -08:00
2e38d53dca
Remove dead line
2023-02-16 16:57:42 -08:00
f77c04ff36
Torchscript fixes/hacks for rms_norm, refactor ParallelScalingBlock with manual combination of input projections, closer paper match
2023-02-16 16:57:42 -08:00
122621daef
Add Final annotation to attn_fas to avoid symbol lookup of new scaled_dot_product_attn fn on old PyTorch in jit
2023-02-16 16:57:42 -08:00
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.
2023-02-16 16:57:42 -08:00
a09d403c24
changed warning to info
2023-02-16 16:20:31 -08:00
8470e29541
Add support to load safetensors weights
2023-02-16 16:20:31 -08:00
624266148d
Remove unused imports from _hub helpers
2023-02-09 17:47:26 -08:00
2cfff0581b
Add grad_checkpointing support to features_only, test in EfficientDet.
2023-02-09 17:45:40 -08:00
9c14654a0d
Improve support for custom dataset label name/description through HF hub export, via pretrained_cfg
2023-02-08 08:29:20 -08:00
0d33127df2
Add 384x384 convnext_large_mlp laion2b fine-tune on in1k
2023-02-06 22:01:04 -08:00
7a0bd095cb
Update model prune loader to use pkgutil
2023-02-06 17:45:16 -08:00
13acac8c5e
Update head metadata for effformerv2
2023-02-04 23:11:51 -08:00
8682528096
Add first conv metadata for efficientformer_v2
2023-02-04 23:02:02 -08:00
72fba669a8
is_scripting() guard on checkpoint_seq
2023-02-04 14:21:49 -08:00
95ec255f7f
Finish timm mode api for efficientformer_v2, add grad checkpointing support to both efficientformers
2023-02-03 21:21:23 -08:00
9d03c6f526
Merge remote-tracking branch 'origin/main' into levit_efficientformer_redux
2023-02-03 14:47:01 -08:00
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.
2023-02-03 14:12:29 -08:00
2cb2699dc8
Apply fix from #1649 to main
2023-02-03 11:28:57 -08:00
b3042081b4
Add laion -> in1k fine-tuned base and large_mlp weights for convnext
2023-02-03 10:58:02 -08:00
316bdf8955
Add mlp head support for convnext_large, add laion2b CLIP weights, prep fine-tuned weight tags
2023-02-01 08:27:02 -08:00
6f28b562c6
Factor NormMlpClassifierHead from MaxxViT and use across MaxxViT / ConvNeXt / DaViT, refactor some type hints & comments
2023-01-27 14:57:01 -08:00
9a53c3f727
Finalize DaViT, some formatting and modelling simplifications (separate PatchEmbed to Stem + Downsample, weights on HF hub.
2023-01-27 13:54:04 -08:00
fb717056da
Merge remote-tracking branch 'upstream/main'
2023-01-26 10:49:15 -08:00
14b84e8895
📝 update docstrings
2023-01-26 00:49:44 -05:00
f0dc8a8267
📝 update docstrings for create_model
2023-01-25 21:10:41 -05:00
64667bfa0e
Add 'gigantic' vit clip variant for feature extraction and future fine-tuning
2023-01-25 18:02:10 -08:00
36989cfae4
Factor out readme generation in hub helper, add more readme fields
2023-01-20 14:49:40 -08:00
32f252381d
Change order of checkpoitn filtering fn application in builder, try dict, model variant first
2023-01-20 14:48:54 -08:00
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.
2023-01-20 14:45:25 -08:00
ca38e1e73f
Update ClassifierHead module, add reset() method, update in_chs -> in_features for consistency
2023-01-20 14:44:05 -08:00
8ab573cd26
Add convnext_tiny and convnext_small 384x384 fine-tunes of in12k weights, fix pool size for laion CLIP convnext weights
2023-01-20 14:40:16 -08:00
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
2023-01-15 14:34:56 -08:00
e9aac412de
Correct mean/std for CLIP convnexts
2023-01-14 22:53:56 -08:00
42bd8f7bcb
Add convnext_base CLIP image tower weights for fine-tuning / features
2023-01-14 21:16:29 -08:00
a2c14c2064
Add tiny/small in12k pretrained and fine-tuned ConvNeXt models
2023-01-11 14:50:39 -08:00
01fdf44438
Initial focalnet import, more refactoring needed for timm.
2023-01-09 16:18:19 -08:00
2e83bba142
Revert head norm changes to ConvNeXt as it broke some downstream use, alternate workaround for fcmae weights
2023-01-09 13:37:40 -08:00
1825b5e314
maxxvit type
2023-01-09 08:57:31 -08:00
5078b28f8a
More kwarg handling tweaks, maxvit_base_rw def added
2023-01-09 08:57:31 -08:00
c0d7388a1b
Improving kwarg merging in more models
2023-01-09 08:57:31 -08:00
60ebb6cefa
Re-order vit pretrained entries for more sensible default weights (no .tag specified)
2023-01-06 16:12:33 -08:00
e861b74cf8
Pass through --model-kwargs (and --opt-kwargs for train) from command line through to model __init__. Update some models to improve arg overlay. Cleanup along the way.
2023-01-06 16:12:33 -08:00
add3fb864e
Working on improved model card template for push_to_hf_hub
2023-01-06 16:12:33 -08:00
6e5553da5f
Add ConvNeXt-V2 support (model additions and weights) ( #1614 )
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* Add ConvNeXt-V2 support (model additions and weights)
* ConvNeXt-V2 weights on HF Hub, tweaking some tests
* Update README, fixing convnextv2 tests
2023-01-05 07:53:32 -08:00
6902c48a5f
Fix ResNet based models to work w/ norm layers w/o affine params. Reformat long arg lists into vertical form.
2022-12-29 16:32:26 -08:00
8ece53e194
Switch BEiT to HF hub weights
2022-12-22 21:43:04 -08:00
9a51e4ea2e
Add FlexiViT models and weights, refactoring, push more weights
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* push all vision_transformer*.py weights to HF hub
* finalize more pretrained tags for pushed weights
* refactor pos_embed files and module locations, move some pos embed modules to layers
* tweak hf hub helpers to aid bulk uploading and updating
2022-12-22 17:23:09 -08:00
10b3f696b4
Davit std ( #6 )
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Separate patch_embed module
2022-12-16 21:50:28 -08:00
656e1776de
Convert mobilenetv3 to multi-weight, tweak PretrainedCfg metadata
2022-12-16 09:29:13 -08:00
6a01101905
Update efficientnet.py and convnext.py to multi-weight, add ImageNet-12k pretrained EfficientNet-B5 and ConvNeXt-Nano.
2022-12-14 20:33:23 -08:00
84178fca60
Merge branch 'rwightman:main' into main
2022-12-12 23:13:58 -08:00
c43340ddd4
Davit std ( #5 )
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* Update davit.py
* Update test_models.py
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* Davit revised (#4 )
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clean up
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2022-12-11 03:03:22 -08:00
d5e7d6b27e
Merge remote-tracking branch 'origin/main' into refactor-imports
2022-12-09 14:49:44 -08:00
cda39b35bd
Add a deprecation phase to module re-org
2022-12-09 14:39:45 -08:00
edea013dd1
Davit std ( #3 )
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Davit with all features working
2022-12-09 02:53:21 -08:00
7c4ed4d5a4
Add EVA-large models
2022-12-08 16:21:30 -08:00
434a03937d
Merge branch 'rwightman:main' into main
2022-12-08 08:05:16 -08:00
98047ef5e3
Add EVA FT results, hopefully fix BEiT test failures
2022-12-07 08:54:06 -08:00
3cc4d7a894
Fix missing register for 224 eva model
2022-12-07 08:54:06 -08:00
eba07b0de7
Add eva models to beit.py
2022-12-07 08:54:06 -08:00
3bd96609c8
Davit ( #1 )
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Implement the davit model from https://arxiv.org/abs/2204.03645 and https://github.com/dingmyu/davit
2022-12-06 17:19:25 -08:00
927f031293
Major module / path restructure, timm.models.layers -> timm.layers, add _ prefix to all non model modules in timm.models
2022-12-06 15:00:06 -08:00
3785c234d7
Remove clip vit models that won't be ft and comment two that aren't uploaded yet
2022-12-05 10:21:34 -08:00
755570e2d6
Rename _pretrained.py -> pretrained.py, not feasible to change the other files to same scheme without breaking uses
2022-12-05 10:21:34 -08:00
72cfa57761
Add ported Tensorflow MaxVit weights. Add a few more CLIP ViT fine-tunes. Tweak some model tag names. Improve model tag name sorting. Update HF hub push config layout.
2022-12-05 10:21:34 -08:00
4d5c395160
MaxVit, ViT, ConvNeXt, and EfficientNet-v2 updates
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* Add support for TF weights and modelling specifics to MaxVit (testing ported weights)
* More fine-tuned CLIP ViT configs
* ConvNeXt and MaxVit updated to new pretrained cfgs use
* EfficientNetV2, MaxVit and ConvNeXt high res models use squash crop/resize
2022-12-05 10:21:34 -08:00
9da7e3a799
Add crop_mode for pretraind config / image transforms. Add support for dynamo compilation to benchmark/train/validate
2022-12-05 10:21:34 -08:00
b2b6285af7
Add two more FT clip weights
2022-12-05 10:21:34 -08:00
5895056dc4
Add openai b32 ft
2022-12-05 10:21:34 -08:00
9dea5143d5
Adding more clip ft variants
2022-12-05 10:21:34 -08:00
444dcba4ad
CLIP B16 12k weights added
2022-12-05 10:21:34 -08:00
dff4717cbf
Add clip b16 384x384 finetunes
2022-12-05 10:21:34 -08:00
883fa2eeaa
Add fine-tuned B/16 224x224 in1k clip models
2022-12-05 10:21:34 -08:00
9a3d2ac2d5
Add latest CLIP ViT fine-tune pretrained configs / model entrypt updates
2022-12-05 10:21:34 -08:00
42bbbddee9
Add missing model config
2022-12-05 10:21:34 -08:00
def68befa7
Updating vit model defs for mult-weight support trial (vit first). Prepping for CLIP (laion2b and openai) fine-tuned weights.
2022-12-05 10:21:34 -08:00
0dadb4a6e9
Initial multi-weight support, handled so old pretraing config handling co-exists with new tags.
2022-12-05 10:21:34 -08:00
9b114754db
refactor push_to_hub helper
2022-11-16 12:03:34 +01:00
ae0a0db7de
Create repo before cloning with Repository.clone_from
2022-11-15 15:17:20 +01:00
803254bb40
Fix spacing misalignment for fast norm path in LayerNorm modules
2022-10-24 21:43:49 -07:00
6635bc3f7d
Merge pull request #1479 from rwightman/script_cleanup
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Train / val script enhancements, non-GPU (ie CPU) device support, HF datasets support, TFDS/WDS dataloading improvements
2022-10-15 09:29:39 -07:00
0e6023f032
Merge pull request #1381 from ChristophReich1996/master
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Fix typo in PositionalEncodingFourier
2022-10-14 18:34:33 -07:00
66f4af7090
Merge remote-tracking branch 'origin/master' into script_cleanup
2022-10-14 15:54:00 -07:00
9914f744dc
Add more maxxvit weights includ ConvNeXt conv block based experiments.
2022-10-10 21:49:18 -07:00
8fda68aff6
Fix repo id bug
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This to fix this issue #1482
2022-10-05 16:26:06 +02:00
1199c5a1a4
clip_laion2b models need 1e-5 eps for LayerNorm
2022-09-25 10:36:54 -07:00
e858912e0c
Add brute-force checkpoint remapping option
2022-09-23 16:07:03 -07:00
b293dfa595
Add CL SE module
2022-09-23 16:06:09 -07:00
a383ef99f5
Make huggingface_hub necessary if it's the only source for a pretrained weight
2022-09-23 13:54:21 -07:00
e069249a2d
Add hf hub entries for laion2b clip models, add huggingface_hub dependency, update some setup/reqs, torch >= 1.7
2022-09-16 21:39:05 -07:00
9d65557be3
Fix errant import
2022-09-15 17:47:23 -07:00
9709dbaaa9
Adding support for fine-tune CLIP LAION-2B image tower weights for B/32, L/14, H/14 and g/14. Still WIP
2022-09-15 17:25:59 -07:00
a520da9b49
Update tresnet features_info for v2
2022-09-13 20:54:54 -07:00
c8ab747bf4
BEiT-V2 checkpoints didn't remove 'module' from weights, adapt checkpoint filter
2022-09-13 17:56:49 -07:00
73049dc2aa
Fix type in dla weight update
2022-09-13 17:52:45 -07:00
e11efa872d
Update a bunch of weights with external links to timm release assets. Fixes issue with *aliyuncs.com returning forbidden. Did pickle scan / verify and re-hash. Add TresNet-V2-L weights.
2022-09-13 16:35:26 -07:00
fa8c84eede
Update maxvit_tiny_256 weight to better iter, add coatnet / maxvit / maxxvit model defs for future runs
2022-09-07 12:37:37 -07:00
c1b3cea19d
Add maxvit_rmlp_tiny_rw_256 model def and weights w/ 84.2 top-1 @ 256, 84.8 @ 320
2022-09-07 10:27:11 -07:00
914544fc81
Add beitv2 224x224 checkpoints from https://github.com/microsoft/unilm/tree/master/beit2
2022-09-06 20:25:18 -07:00
dc90816f26
Add `maxvit_tiny_rw_224` weights 83.5 @ 224 and `maxvit_rmlp_pico_rw_256` relpos weights, 80.5 @ 256, 81.3 @ 320
2022-09-06 16:14:41 -07:00
f489f02ad1
Make gcvit window size ratio based to improve resolution changing support #1449 . Change default init to original.
2022-09-06 16:14:00 -07:00
7f1b223c02
Add maxvit_rmlp_nano_rw_256 model def & weights, make window/grid size dynamic wrt img_size by default
2022-08-29 15:49:32 -07:00
e6a4361306
pretrained_cfg entry for mvitv2_small_cls
2022-08-28 15:27:01 -07:00
f66e5f0e35
Fix class token support in MViT-V2, add small_class variant to ensure it's tested. Fix #1443
2022-08-28 15:24:04 -07:00
f1d2160d85
Update a few maxxvit comments, rename PartitionAttention -> PartitionAttenionCl for consistency with other blocks
2022-08-26 12:53:49 -07:00
eca6f0a25c
Fix syntax error (extra dataclass comma) in maxxvit.py
2022-08-26 11:29:09 -07:00
ff6a919cf5
Add --fast-norm arg to benchmark.py, train.py, validate.py
2022-08-25 17:20:46 -07:00
769ab4b98a
Clean up no_grad for trunc normal weight inits
2022-08-25 16:29:52 -07:00
48e1df8b37
Add norm/norm_act header comments
2022-08-25 16:29:34 -07:00
7c2660576d
Tweak init for convnext block using maxxvit/coatnext.
2022-08-25 15:30:59 -07:00
1d8d6f6072
Fix two default args in DenseNet blocks... fix #1427
2022-08-25 15:00:35 -07:00
527f9a4cb2
Updated to correct maxvit_nano weights...
2022-08-24 12:42:11 -07:00
b2e8426fca
Make k=stride=2 ('avg2') pooling default for coatnet/maxvit. Add weight links. Rename 'combined' partition to 'parallel'.
2022-08-24 11:01:20 -07:00
837c68263b
For ConvNeXt, use timm internal LayerNorm for fast_norm in non conv_mlp mode
2022-08-23 15:17:12 -07:00
cac0a4570a
More test fixes, pool size for 256x256 maxvit models
2022-08-23 13:38:26 -07:00
e939ed19b9
Rename internal creation fn for maxvit, has not been just coatnet for a while...
2022-08-22 17:44:51 -07:00
ffaf97f813
MaxxVit! A very configurable MaxVit and CoAtNet impl with lots of goodies..
2022-08-22 17:42:10 -07:00
8c9696c9df
More model and test fixes
2022-08-22 17:40:31 -07:00
ca52108c2b
Fix some model support functions
2022-08-19 10:20:51 -07:00
f332fc2db7
Fix some test failures, torchscript issues
2022-08-18 16:19:46 -07:00
6e559e9b5f
Add MViT (Multi-Scale) V2
2022-08-17 15:12:31 -07:00
43aa84e861
Add 'fast' layer norm that doesn't cast to float32, support APEX LN impl for slight speed gain, update norm and act factories, tweak SE for ability to disable bias (needed by GCVit)
2022-08-17 14:32:58 -07:00
Ross Wightman
Ross Wightman
Leng Yue
Marco Forte
Piotr Sebastian Kluska
Benjamin Bossan
testbot
Fredo Guan
nateraw
Wauplin
Mohamed Rashad