5.6 KiB
Hybrid Vision Transformer (ViT Hybrid)
Overview
The hybrid Vision Transformer (ViT) model was proposed in An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby. It's the first paper that successfully trains a Transformer encoder on ImageNet, attaining very good results compared to familiar convolutional architectures. ViT hybrid is a slight variant of the plain Vision Transformer, by leveraging a convolutional backbone (specifically, BiT) whose features are used as initial "tokens" for the Transformer.
The abstract from the paper is the following:
While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train.
This model was contributed by nielsr. The original code (written in JAX) can be found here.
Using Scaled Dot Product Attention (SDPA)
PyTorch includes a native scaled dot-product attention (SDPA) operator as part of torch.nn.functional. This function
encompasses several implementations that can be applied depending on the inputs and the hardware in use. See the
official documentation
or the GPU Inference
page for more information.
SDPA is used by default for torch>=2.1.1 when an implementation is available, but you may also set
attn_implementation="sdpa" in from_pretrained() to explicitly request SDPA to be used.
from transformers import ViTHybridForImageClassification
model = ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384", attn_implementation="sdpa", torch_dtype=torch.float16)
...
For the best speedups, we recommend loading the model in half-precision (e.g. torch.float16 or torch.bfloat16).
On a local benchmark (A100-40GB, PyTorch 2.3.0, OS Ubuntu 22.04) with float32 and google/vit-hybrid-base-bit-384 model, we saw the following speedups during inference.
| Batch size | Average inference time (ms), eager mode | Average inference time (ms), sdpa model | Speed up, Sdpa / Eager (x) |
|---|---|---|---|
| 1 | 29 | 18 | 1.61 |
| 2 | 26 | 18 | 1.44 |
| 4 | 25 | 18 | 1.39 |
| 8 | 34 | 24 | 1.42 |
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ViT Hybrid.
- [
ViTHybridForImageClassification] is supported by this example script and notebook. - See also: Image classification task guide
If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
ViTHybridConfig
autodoc ViTHybridConfig
ViTHybridImageProcessor
autodoc ViTHybridImageProcessor - preprocess
ViTHybridModel
autodoc ViTHybridModel - forward
ViTHybridForImageClassification
autodoc ViTHybridForImageClassification - forward