[tune](deps): Bump timm from 0.4.5 to 0.6.7 in /python/requirements/ml

[dependabot]

Bumps timm from 0.4.5 to 0.6.7.

Release notes

Sourced from timm's releases.

v0.6.7 Release

Minor bug fixes and a few more weights since 0.6.5

• A few more weights & model defs added:
  • darknetaa53 - 79.8 @ 256, 80.5 @ 288
  • convnext_nano - 80.8 @ 224, 81.5 @ 288
  • cs3sedarknet_l - 81.2 @ 256, 81.8 @ 288
  • cs3darknet_x - 81.8 @ 256, 82.2 @ 288
  • cs3sedarknet_x - 82.2 @ 256, 82.7 @ 288
  • cs3edgenet_x - 82.2 @ 256, 82.7 @ 288
  • cs3se_edgenet_x - 82.8 @ 256, 83.5 @ 320
• cs3* weights above all trained on TPU w/ bits_and_tpu branch. Thanks to TRC program!
• Add output_stride=8 and 16 support to ConvNeXt (dilation)
• deit3 models not being able to resize pos_emb fixed

v0.6.5 Release

First official release in a long while (since 0.5.4). All change log since 0.5.4 below,

July 8, 2022

More models, more fixes

• Official research models (w/ weights) added:
  • EdgeNeXt from (https://github.com/mmaaz60/EdgeNeXt)
  • MobileViT-V2 from (https://github.com/apple/ml-cvnets)
  • DeiT III (Revenge of the ViT) from (https://github.com/facebookresearch/deit)
• My own models:
  • Small ResNet defs added by request with 1 block repeats for both basic and bottleneck (resnet10 and resnet14)
  • CspNet refactored with dataclass config, simplified CrossStage3 (cs3) option. These are closer to YOLO-v5+ backbone defs.
  • More relative position vit fiddling. Two srelpos (shared relative position) models trained, and a medium w/ class token.
  • Add an alternate downsample mode to EdgeNeXt and train a small model. Better than original small, but not their new USI trained weights.
• My own model weight results (all ImageNet-1k training)
  • resnet10t - 66.5 @ 176, 68.3 @ 224
  • resnet14t - 71.3 @ 176, 72.3 @ 224
  • resnetaa50 - 80.6 @ 224 , 81.6 @ 288
  • darknet53 - 80.0 @ 256, 80.5 @ 288
  • cs3darknet_m - 77.0 @ 256, 77.6 @ 288
  • cs3darknet_focus_m - 76.7 @ 256, 77.3 @ 288
  • cs3darknet_l - 80.4 @ 256, 80.9 @ 288
  • cs3darknet_focus_l - 80.3 @ 256, 80.9 @ 288
  • vit_srelpos_small_patch16_224 - 81.1 @ 224, 82.1 @ 320
  • vit_srelpos_medium_patch16_224 - 82.3 @ 224, 83.1 @ 320
  • vit_relpos_small_patch16_cls_224 - 82.6 @ 224, 83.6 @ 320
  • edgnext_small_rw - 79.6 @ 224, 80.4 @ 320
• cs3, darknet, and vit_*relpos weights above all trained on TPU thanks to TRC program! Rest trained on overheating GPUs.
• Hugging Face Hub support fixes verified, demo notebook TBA
• Pretrained weights / configs can be loaded externally (ie from local disk) w/ support for head adaptation.
• Add support to change image extensions scanned by timm datasets/parsers. See (rwightman/pytorch-image-models#1274)
• Default ConvNeXt LayerNorm impl to use F.layer_norm(x.permute(0, 2, 3, 1), ...).permute(0, 3, 1, 2) via LayerNorm2d in all cases.
  • a bit slower than previous custom impl on some hardware (ie Ampere w/ CL), but overall fewer regressions across wider HW / PyTorch version ranges.
  • previous impl exists as LayerNormExp2d in models/layers/norm.py
• Numerous bug fixes

... (truncated)

Changelog

Sourced from timm's changelog.

• Version 0.6.7 PyPi release (/w above bug fixes and new weighs since 0.6.5)

July 8, 2022

More models, more fixes

• Official research models (w/ weights) added:
  • EdgeNeXt from (https://github.com/mmaaz60/EdgeNeXt)
  • MobileViT-V2 from (https://github.com/apple/ml-cvnets)
  • DeiT III (Revenge of the ViT) from (https://github.com/facebookresearch/deit)
• My own models:
  • Small ResNet defs added by request with 1 block repeats for both basic and bottleneck (resnet10 and resnet14)
  • CspNet refactored with dataclass config, simplified CrossStage3 (cs3) option. These are closer to YOLO-v5+ backbone defs.
  • More relative position vit fiddling. Two srelpos (shared relative position) models trained, and a medium w/ class token.
  • Add an alternate downsample mode to EdgeNeXt and train a small model. Better than original small, but not their new USI trained weights.
• My own model weight results (all ImageNet-1k training)
  • resnet10t - 66.5 @ 176, 68.3 @ 224
  • resnet14t - 71.3 @ 176, 72.3 @ 224
  • resnetaa50 - 80.6 @ 224 , 81.6 @ 288
  • darknet53 - 80.0 @ 256, 80.5 @ 288
  • cs3darknet_m - 77.0 @ 256, 77.6 @ 288
  • cs3darknet_focus_m - 76.7 @ 256, 77.3 @ 288
  • cs3darknet_l - 80.4 @ 256, 80.9 @ 288
  • cs3darknet_focus_l - 80.3 @ 256, 80.9 @ 288
  • vit_srelpos_small_patch16_224 - 81.1 @ 224, 82.1 @ 320
  • vit_srelpos_medium_patch16_224 - 82.3 @ 224, 83.1 @ 320
  • vit_relpos_small_patch16_cls_224 - 82.6 @ 224, 83.6 @ 320
  • edgnext_small_rw - 79.6 @ 224, 80.4 @ 320
• cs3, darknet, and vit_*relpos weights above all trained on TPU thanks to TRC program! Rest trained on overheating GPUs.
• Hugging Face Hub support fixes verified, demo notebook TBA
• Pretrained weights / configs can be loaded externally (ie from local disk) w/ support for head adaptation.
• Add support to change image extensions scanned by timm datasets/parsers. See (rwightman/pytorch-image-models#1274)
• Default ConvNeXt LayerNorm impl to use F.layer_norm(x.permute(0, 2, 3, 1), ...).permute(0, 3, 1, 2) via LayerNorm2d in all cases.
  • a bit slower than previous custom impl on some hardware (ie Ampere w/ CL), but overall fewer regressions across wider HW / PyTorch version ranges.
  • previous impl exists as LayerNormExp2d in models/layers/norm.py
• Numerous bug fixes
• Currently testing for imminent PyPi 0.6.x release
• LeViT pretraining of larger models still a WIP, they don't train well / easily without distillation. Time to add distill support (finally)?
• ImageNet-22k weight training + finetune ongoing, work on multi-weight support (slowly) chugging along (there are a LOT of weights, sigh) ...

May 13, 2022

• Official Swin-V2 models and weights added from (https://github.com/microsoft/Swin-Transformer). Cleaned up to support torchscript.
• Some refactoring for existing timm Swin-V2-CR impl, will likely do a bit more to bring parts closer to official and decide whether to merge some aspects.
• More Vision Transformer relative position / residual post-norm experiments (all trained on TPU thanks to TRC program)
  • vit_relpos_small_patch16_224 - 81.5 @ 224, 82.5 @ 320 -- rel pos, layer scale, no class token, avg pool
  • vit_relpos_medium_patch16_rpn_224 - 82.3 @ 224, 83.1 @ 320 -- rel pos + res-post-norm, no class token, avg pool
  • vit_relpos_medium_patch16_224 - 82.5 @ 224, 83.3 @ 320 -- rel pos, layer scale, no class token, avg pool
  • vit_relpos_base_patch16_gapcls_224 - 82.8 @ 224, 83.9 @ 320 -- rel pos, layer scale, class token, avg pool (by mistake)
• Bring 512 dim, 8-head 'medium' ViT model variant back to life (after using in a pre DeiT 'small' model for first ViT impl back in 2020)
• Add ViT relative position support for switching btw existing impl and some additions in official Swin-V2 impl for future trials
• Sequencer2D impl (https://arxiv.org/abs/2205.01972), added via PR from author (https://github.com/okojoalg)

... (truncated)

Commits

• 7cd4204 Add TPU TRC acknowledge
• 7d44d65 Update README and changelogs
• d875a1d version 0.6.7
• c865028 Update benchmark with latest model adds
• 30bd174 Improve csv table result processing for better sort when updating
• e987e29 Add convnext_nano and few cs3 models to existing results tables
• 6f103a4 Add convnext_nano weights, 80.8 @ 224, 81.5 @ 288
• 4042a94 Add weights for two 'Edge' block (3x3->1x1) variants of CS3 networks.
• c8f69e0 Merge pull request #1365 from veritable-tech/fix-resize-pos-embed
• 99af63c Merge pull request #1277 from lukasugar/patch-1
• Additional commits viewable in compare view

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