[tune](deps): Bump timm from 0.4.5 to 0.5.4 in /python/requirements/ml

[dependabot]

Bumps timm from 0.4.5 to 0.5.4.

Changelog

Sourced from timm's changelog.

• Version 0.5.4 w/ release to be pushed to pypi. It's been a while since last pypi update and riskier changes will be merged to main branch soon....
• Add ConvNeXT models /w weights from official impl (https://github.com/facebookresearch/ConvNeXt), a few perf tweaks, compatible with timm features
• Tried training a few small (~1.8-3M param) / mobile optimized models, a few are good so far, more on the way...
  • mnasnet_small - 65.6 top-1
  • mobilenetv2_050 - 65.9
  • lcnet_100/075/050 - 72.1 / 68.8 / 63.1
  • semnasnet_075 - 73
  • fbnetv3_b/d/g - 79.1 / 79.7 / 82.0
• TinyNet models added by rsomani95
• LCNet added via MobileNetV3 architecture

Nov 22, 2021

• A number of updated weights anew new model defs
  • eca_halonext26ts - 79.5 @ 256
  • resnet50_gn (new) - 80.1 @ 224, 81.3 @ 288
  • resnet50 - 80.7 @ 224, 80.9 @ 288 (trained at 176, not replacing current a1 weights as default since these don't scale as well to higher res, weights)
  • resnext50_32x4d - 81.1 @ 224, 82.0 @ 288
  • sebotnet33ts_256 (new) - 81.2 @ 224
  • lamhalobotnet50ts_256 - 81.5 @ 256
  • halonet50ts - 81.7 @ 256
  • halo2botnet50ts_256 - 82.0 @ 256
  • resnet101 - 82.0 @ 224, 82.8 @ 288
  • resnetv2_101 (new) - 82.1 @ 224, 83.0 @ 288
  • resnet152 - 82.8 @ 224, 83.5 @ 288
  • regnetz_d8 (new) - 83.5 @ 256, 84.0 @ 320
  • regnetz_e8 (new) - 84.5 @ 256, 85.0 @ 320
• vit_base_patch8_224 (85.8 top-1) & in21k variant weights added thanks Martins Bruveris
• Groundwork in for FX feature extraction thanks to Alexander Soare
  • models updated for tracing compatibility (almost full support with some distlled transformer exceptions)

Oct 19, 2021

• ResNet strikes back (https://arxiv.org/abs/2110.00476) weights added, plus any extra training components used. Model weights and some more details here (https://github.com/rwightman/pytorch-image-models/releases/tag/v0.1-rsb-weights)
• BCE loss and Repeated Augmentation support for RSB paper
• 4 series of ResNet based attention model experiments being added (implemented across byobnet.py/byoanet.py). These include all sorts of attention, from channel attn like SE, ECA to 2D QKV self-attention layers such as Halo, Bottlneck, Lambda. Details here (https://github.com/rwightman/pytorch-image-models/releases/tag/v0.1-attn-weights)
• Working implementations of the following 2D self-attention modules (likely to be differences from paper or eventual official impl):
  • Halo (https://arxiv.org/abs/2103.12731)
  • Bottleneck Transformer (https://arxiv.org/abs/2101.11605)
  • LambdaNetworks (https://arxiv.org/abs/2102.08602)
• A RegNetZ series of models with some attention experiments (being added to). These do not follow the paper (https://arxiv.org/abs/2103.06877) in any way other than block architecture, details of official models are not available. See more here (https://github.com/rwightman/pytorch-image-models/releases/tag/v0.1-attn-weights)
• ConvMixer (https://openreview.net/forum?id=TVHS5Y4dNvM), CrossVit (https://arxiv.org/abs/2103.14899), and BeiT (https://arxiv.org/abs/2106.08254) architectures + weights added
• freeze/unfreeze helpers by Alexander Soare

Aug 18, 2021

• Optimizer bonanza!
  • Add LAMB and LARS optimizers, incl trust ratio clipping options. Tweaked to work properly in PyTorch XLA (tested on TPUs w/ timm bits branch)
  • Add MADGRAD from FB research w/ a few tweaks (decoupled decay option, step handling that works with PyTorch XLA)
  • Some cleanup on all optimizers and factory. No more .data, a bit more consistency, unit tests for all!
  • SGDP and AdamP still won't work with PyTorch XLA but others should (have yet to test Adabelief, Adafactor, Adahessian myself).
• EfficientNet-V2 XL TF ported weights added, but they don't validate well in PyTorch (L is better). The pre-processing for the V2 TF training is a bit diff and the fine-tuned 21k -> 1k weights are very sensitive and less robust than the 1k weights.
• Added PyTorch trained EfficientNet-V2 'Tiny' w/ GlobalContext attn weights. Only .1-.2 top-1 better than the SE so more of a curiosity for those interested.

... (truncated)

Commits

• e0c4eec Default conv_mlp to False across the board for ConvNeXt, causing issues on mo...
• b669f4a Add ConvNeXt 22k->1k fine-tuned and 384 22k-1k fine-tuned weights after testing
• 6dcbaf2 Update README.md
• a8d103e Giant/gigantic vits snuck through in a test a broke GitHub test runner, add f...
• ef72ad4 Extra vit_huge model likely to cause test issue (non in21k variant), adding t...
• e967c72 Update REAMDE.md. Sneak in g/G (giant / gigantic?) ViT defs from scaling paper
• 9ca3437 Add some more small model weights lcnet, mnas, mnv2
• fa6463c Version 0.5.4
• fa81164 Fix stem width for really small mobilenetv3 arch defs
• edd3d73 Add missing dropout for head reset in ConvNeXt default head
• Additional commits viewable in compare view

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