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Add BEiT (#12994)

Browse Source 
* First pass

* Make conversion script work

* Improve conversion script

* Fix bug, conversion script working

* Improve conversion script, implement BEiTFeatureExtractor

* Make conversion script work based on URL

* Improve conversion script

* Add tests, add documentation

* Fix bug in conversion script

* Fix another bug

* Add support for converting masked image modeling model

* Add support for converting masked image modeling

* Fix bug

* Add print statement for debugging

* Fix another bug

* Make conversion script finally work for masked image modeling models

* Move id2label for datasets to JSON files on the hub

* Make sure id's are read in as integers

* Add integration tests

* Make style & quality

* Fix test, add BEiT to README

* Apply suggestions from @sgugger's review

* Apply suggestions from code review

* Make quality

* Replace nielsr by microsoft in tests, add docs

* Rename BEiT to Beit

* Minor fix

* Fix docs of BeitForMaskedImageModeling

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
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NielsRogge
2021-08-04 18:29:23 +02:00
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..
Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
BEiT
-----------------------------------------------------------------------------------------------------------------------
Overview
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The BEiT model was proposed in `BEiT: BERT Pre-Training of Image Transformers <https://arxiv.org/abs/2106.08254>`__ by
Hangbo Bao, Li Dong and Furu Wei. Inspired by BERT, BEiT is the first paper that makes self-supervised pre-training of
Vision Transformers (ViTs) outperform supervised pre-training. Rather than pre-training the model to predict the class
of an image (as done in the `original ViT paper <https://arxiv.org/abs/2010.11929>`__), BEiT models are pre-trained to
predict visual tokens from the codebook of OpenAI's `DALL-E model <https://arxiv.org/abs/2102.12092>`__ given masked
patches.
The abstract from the paper is the following:
*We introduce a self-supervised vision representation model BEiT, which stands for Bidirectional Encoder representation
from Image Transformers. Following BERT developed in the natural language processing area, we propose a masked image
modeling task to pretrain vision Transformers. Specifically, each image has two views in our pre-training, i.e, image
patches (such as 16x16 pixels), and visual tokens (i.e., discrete tokens). We first "tokenize" the original image into
visual tokens. Then we randomly mask some image patches and fed them into the backbone Transformer. The pre-training
objective is to recover the original visual tokens based on the corrupted image patches. After pre-training BEiT, we
directly fine-tune the model parameters on downstream tasks by appending task layers upon the pretrained encoder.
Experimental results on image classification and semantic segmentation show that our model achieves competitive results
with previous pre-training methods. For example, base-size BEiT achieves 83.2% top-1 accuracy on ImageNet-1K,
significantly outperforming from-scratch DeiT training (81.8%) with the same setup. Moreover, large-size BEiT obtains
86.3% only using ImageNet-1K, even outperforming ViT-L with supervised pre-training on ImageNet-22K (85.2%).*
Tips:
- BEiT models are regular Vision Transformers, but pre-trained in a self-supervised way rather than supervised. They
outperform both the original model (ViT) as well as Data-efficient Image Transformers (DeiT) when fine-tuned on
ImageNet-1K and CIFAR-100.
- As the BEiT models expect each image to be of the same size (resolution), one can use
:class:`~transformers.BeitFeatureExtractor` to resize (or rescale) and normalize images for the model.
- Both the patch resolution and image resolution used during pre-training or fine-tuning are reflected in the name of
each checkpoint. For example, :obj:`microsoft/beit-base-patch16-224` refers to a base-sized architecture with patch
resolution of 16x16 and fine-tuning resolution of 224x224. All checkpoints can be found on the `hub
<https://huggingface.co/models?search=microsoft/beit>`__.
- The available checkpoints are either (1) pre-trained on `ImageNet-22k <http://www.image-net.org/>`__ (a collection of
14 million images and 22k classes) only, (2) also fine-tuned on ImageNet-22k or (3) also fine-tuned on `ImageNet-1k
<http://www.image-net.org/challenges/LSVRC/2012/>`__ (also referred to as ILSVRC 2012, a collection of 1.3 million
images and 1,000 classes).
- BEiT uses relative position embeddings, inspired by the T5 model. During pre-training, the authors shared the
relative position bias among the several self-attention layers. During fine-tuning, each layer's relative position
bias is initialized with the shared relative position bias obtained after pre-training. Note that, if one wants to
pre-train a model from scratch, one needs to either set the :obj:`use_relative_position_bias` or the
:obj:`use_relative_position_bias` attribute of :class:`~transformers.BeitConfig` to :obj:`True` in order to add
position embeddings.
This model was contributed by `nielsr <https://huggingface.co/nielsr>`__. The original code can be found `here
<https://github.com/microsoft/unilm/tree/master/beit>`__.
BeitConfig
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.BeitConfig
:members:
BeitFeatureExtractor
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.BeitFeatureExtractor
:members: __call__
BeitModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.BeitModel
:members: forward
BeitForMaskedImageModeling
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.BeitForMaskedImageModeling
:members: forward
BeitForImageClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.BeitForImageClassification
:members: forward