[tune](deps): Bump transformers from 4.8.1 to 4.18.0 in /python/requirements/tune

[dependabot]

Bumps transformers from 4.8.1 to 4.18.0.

Release notes

Sourced from transformers's releases.

v4.18.0: Checkpoint sharding, vision models

New model additions

You'll notice that we are starting to add several older models in vision. This is because those models are used as backbones in recent architectures. While we could rely on existing libraries for such pretrained models, we will ultimately need some support for those backbones in PyTorch/TensorFlow and Jax, and there is currently no library that supports those three frameworks. This is why we are starting to add those models to Transformers directly (here ResNet and VAN)

GLPN

The GLPN model was proposed in Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim. GLPN combines SegFormer’s hierarchical mix-Transformer with a lightweight decoder for monocular depth estimation. The proposed decoder shows better performance than the previously proposed decoders, with considerably less computational complexity.

• Add GLPN by @​NielsRogge in huggingface/transformers#16199

ResNet

The ResNet model was proposed in Deep Residual Learning for Image Recognition by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun. Our implementation follows the small changes made by Nvidia, we apply the stride=2 for downsampling in bottleneck’s 3x3 conv and not in the first 1x1. This is generally known as “ResNet v1.5”.

ResNet introduced residual connections, they allow to train networks with an unseen number of layers (up to 1000). ResNet won the 2015 ILSVRC & COCO competition, one important milestone in deep computer vision.

• Resnet by @​FrancescoSaverioZuppichini in huggingface/transformers#15770

VAN

The VAN model was proposed in Visual Attention Network by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.

This paper introduces a new attention layer based on convolution operations able to capture both local and distant relationships. This is done by combining normal and large kernel convolution layers. The latter uses a dilated convolution to capture distant correlations.

• Visual Attention Network (VAN) by @​FrancescoSaverioZuppichini in huggingface/transformers#16027

VisionTextDualEncoder

The VisionTextDualEncoderModel can be used to initialize a vision-text dual encoder model with any pretrained vision autoencoding model as the vision encoder (e.g. ViT, BEiT, DeiT) and any pretrained text autoencoding model as the text encoder (e.g. RoBERTa, BERT). Two projection layers are added on top of both the vision and text encoder to project the output embeddings to a shared latent space. The projection layers are randomly initialized so the model should be fine-tuned on a downstream task. This model can be used to align the vision-text embeddings using CLIP like contrastive image-text training and then can be used for zero-shot vision tasks such image-classification or retrieval.

In LiT: Zero-Shot Transfer with Locked-image Text Tuning it is shown how leveraging pre-trained (locked/frozen) image and text model for contrastive learning yields significant improvment on new zero-shot vision tasks such as image classification or retrieval.

• add VisionTextDualEncoder and CLIP fine-tuning script by @​patil-suraj in huggingface/transformers#15701

DiT

DiT was proposed in DiT: Self-supervised Pre-training for Document Image Transformer by Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei. DiT applies the self-supervised objective of BEiT (BERT pre-training of Image Transformers) to 42 million document images, allowing for state-of-the-art results on tasks including:

• document image classification: the RVL-CDIP dataset (a collection of 400,000 images belonging to one of 16 classes).
• document layout analysis: the PubLayNet dataset (a collection of more than 360,000 document images constructed by automatically parsing PubMed XML files).
• table detection: the ICDAR 2019 cTDaR dataset (a collection of 600 training images and 240 testing images).

• Add Document Image Transformer (DiT) by @​NielsRogge in huggingface/transformers#15984

DPT

The DPT model was proposed in Vision Transformers for Dense Prediction by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun. DPT is a model that leverages the Vision Transformer (ViT) as backbone for dense prediction tasks like semantic segmentation and depth estimation.

• Add DPT by @​NielsRogge in huggingface/transformers#15991

... (truncated)

Commits

• 31ec2cb Release: v4.18.0
• b9bf91a Revert "Allow the same config in the auto mapping"
• b1a7dfe Allow the same config in the auto mapping
• 2aef4cf Fix TFTransfoXLLMHeadModel outputs (#16590)
• 8d57c42 [FlaxSpeechEncoderDecoderModel] More Rigorous PT-Flax Equivalence Tests (#16589)
• c656331 [Speech2Text Doc] Fix docs (#16611)
• fb3d0df typo (#16621)
• ae6a7a7 Use CLIP model config to set some kwargs for components (#16609)
• 47c5c05 don't load state_dict twice when using low_cpu_mem_usage in from_pretrained ...
• a2b7d19 Fix seq2seq doc tests (#16606)
• Additional commits viewable in compare view

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

---

Dependabot commands and options

You can trigger Dependabot actions by commenting on this PR:

• @dependabot rebase will rebase this PR
• @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
• @dependabot merge will merge this PR after your CI passes on it
• @dependabot squash and merge will squash and merge this PR after your CI passes on it
• @dependabot cancel merge will cancel a previously requested merge and block automerging
• @dependabot reopen will reopen this PR if it is closed
• @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
• @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
• @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
• @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)

[dependabot]

Superseded by #71.