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408b2d2bd0
* First draft * Update self-attention of RoBERTa as proposition * Improve conversion script * Add TrOCR decoder-only model * More improvements * Make forward pass with pretrained weights work * More improvements * Some more improvements * More improvements * Make conversion work * Clean up print statements * Add documentation, processor * Add test files * Small improvements * Some more improvements * Make fix-copies, improve docs * Make all vision encoder decoder model tests pass * Make conversion script support other models * Update URL for OCR image * Update conversion script * Fix style & quality * Add support for the large-printed model * Fix some issues * Add print statement for debugging * Add print statements for debugging * Make possible fix for sinusoidal embedding * Further debugging * Potential fix v2 * Add more print statements for debugging * Add more print statements for debugging * Deubg more * Comment out print statements * Make conversion of large printed model possible, address review comments * Make it possible to convert the stage1 checkpoints * Clean up code, apply suggestions from code review * Apply suggestions from code review, use Microsoft models in tests * Rename encoder_hidden_size to cross_attention_hidden_size * Improve docs
96 lines
4.5 KiB
ReStructuredText
96 lines
4.5 KiB
ReStructuredText
..
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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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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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TrOCR
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-----------------------------------------------------------------------------------------------------------------------
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The TrOCR model was proposed in `TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
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<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. TrOCR consists of an image Transformer encoder and an autoregressive text Transformer decoder to
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perform `optical character recognition (OCR) <https://en.wikipedia.org/wiki/Optical_character_recognition>`__.
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Please refer to the :doc:`VisionEncoderDecoder <visionencoderdecoder>` class on how to use this model.
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This model was contributed by `Niels Rogge <https://huggingface.co/nielsr>`__.
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The original code can be found `here
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<https://github.com/microsoft/unilm/tree/6f60612e7cc86a2a1ae85c47231507a587ab4e01/trocr>`__.
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Tips:
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- TrOCR is pre-trained in 2 stages before being fine-tuned on downstream datasets. It achieves state-of-the-art results
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on both printed (e.g. the `SROIE dataset <https://paperswithcode.com/dataset/sroie>`__) and handwritten (e.g. the
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`IAM Handwriting dataset <https://fki.tic.heia-fr.ch/databases/iam-handwriting-database>`__) text recognition tasks.
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For more information, see the `official models <https://huggingface.co/models?other=trocr>`__.
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- TrOCR is always used within the :doc:`VisionEncoderDecoder <visionencoderdecoder>` framework.
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Inference
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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TrOCR's :class:`~transformers.VisionEncoderDecoderModel` model accepts images as input and makes use of
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:func:`~transformers.generation_utils.GenerationMixin.generate` to autoregressively generate text given the input
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image.
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The :class:`~transformers.ViTFeatureExtractor` class is responsible for preprocessing the input image and
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:class:`~transformers.RobertaTokenizer` decodes the generated target tokens to the target string. The
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:class:`~transformers.TrOCRProcessor` wraps :class:`~transformers.ViTFeatureExtractor` and
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:class:`~transformers.RobertaTokenizer` into a single instance to both extract the input features and decode the
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predicted token ids.
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- Step-by-step Optical Character Recognition (OCR)
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.. code-block::
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>>> from transformers import TrOCRProcessor, VisionEncoderDecoderModel
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>>> import requests
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>>> from PIL import Image
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>>> processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
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>>> model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")
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>>> # load image from the IAM dataset
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>>> url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"
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>>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
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>>> pixel_values = processor(image, return_tensors="pt").pixel_values
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>>> generated_ids = model.generate(pixel_values)
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>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
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See the `model hub <https://huggingface.co/models?filter=trocr>`__ to look for TrOCR checkpoints.
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TrOCRConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TrOCRConfig
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:members:
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TrOCRProcessor
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TrOCRProcessor
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:members: __call__, from_pretrained, save_pretrained, batch_decode, decode, as_target_processor
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TrOCRForCausalLM
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TrOCRForCausalLM
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:members: forward
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