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* Start the work on TFVisionEncoderDecoderModel * Expose TFVisionEncoderDecoderModel * fix import * Add modeling_tf_vision_encoder_decoder to _ignore_modules in get_model_modules() * reorder * Apply the fix for checkpoint loading as in #14016 * remove attention_mask + fix VISION_DUMMY_INPUTS * A minimal change to make TF generate() work for vision models as encoder in encoder-decoder setting * fix wrong condition: shape_list(input_ids) == 2 * add tests * use personal TFViTModel checkpoint (for now) * Add equivalence tests + projection layer * style * make sure projection layer can run * Add examples * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Clean comments (need to work on TODOs for PyTorch models) * Remove TF -> PT in check_pt_tf_equivalence for TFVisionEncoderDecoderModel * fixes * Revert changes in PT code. * Update tests/test_modeling_tf_vision_encoder_decoder.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Add test_inference_coco_en for TF test * fix quality * fix name * build doc * add main_input_name * Fix ckpt name in test * fix diff between master and this PR * fix doc * fix style and quality * fix missing doc * fix labels handling * Delete auto.rst * Add the changes done in #14016 * fix prefix * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * make style Co-authored-by: ydshieh <ydshieh@users.noreply.github.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
47 lines
1.8 KiB
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47 lines
1.8 KiB
Plaintext
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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specific language governing permissions and limitations under the License.
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# Vision Encoder Decoder Models
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The [`VisionEncoderDecoderModel`] can be used to initialize an image-to-text-sequence model with any
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pretrained vision autoencoding model as the encoder (*e.g.* [ViT](vit), [BEiT](beit), [DeiT](deit))
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and any pretrained language model as the decoder (*e.g.* [RoBERTa](roberta), [GPT2](gpt2), [BERT](bert)).
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The effectiveness of initializing image-to-text-sequence models with pretrained checkpoints has been shown in (for
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example) [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang,
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Zhoujun Li, Furu Wei.
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An example of how to use a [`VisionEncoderDecoderModel`] for inference can be seen in [TrOCR](trocr).
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## VisionEncoderDecoderConfig
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[[autodoc]] VisionEncoderDecoderConfig
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## VisionEncoderDecoderModel
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[[autodoc]] VisionEncoderDecoderModel
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- forward
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- from_encoder_decoder_pretrained
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## TFVisionEncoderDecoderModel
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[[autodoc]] TFVisionEncoderDecoderModel
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- call
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- from_encoder_decoder_pretrained
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## FlaxVisionEncoderDecoderModel
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[[autodoc]] FlaxVisionEncoderDecoderModel
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- __call__
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- from_encoder_decoder_pretrained
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