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* First commit * Add auto-translation with GPT-4 * make fixup * Add a functional layernorm for TF * Add all the auxiliary imports etc. * Add the extra processor and tests * rebase to main * Add all the needed fixes to the GPT code * make fixup * Make convolutions channels-last so they run on CPU * make fixup * Fix final issues * Fix other models affected by test change * Clarify comment on the sparse_prompt_embeddings check * Refactor functional_layernorm, use shape_list in place of .shape in some places * Remove deprecated torch-alike code * Update tests/models/sam/test_modeling_tf_sam.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update tests/models/sam/test_modeling_tf_sam.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Refactor processor with common methods and separated private methods * make fixup * Quietly delete the file that didn't do anything (sorry Sylvain) * Refactor the processor tests into one file * make fixup * Clean up some unnecessary indirection * Fix TF mask postprocessing * Add more processor equivalence tests * Refactor generate_crop_boxes to use framework-neutral np code * Make the serving output correctly conditional * Fix error message line length * Use dict keys rather than indices internally in both TF and PT SAM call/forward * Return dicts internally in the call/forward methods * Revert changes to common tests and just override check_pt_tf_outputs * Revert changes to other model tests * Clarify comments for functional layernorm * Add missing transpose from PT code * Removed unused copied from in PT code * Remove overrides for tests that don't exist in TF * Fix transpose and update tests for PT and TF to check pred_masks * Add training flag * Update tests to use TF checkpoints * Update index.mdx * Add missing cross-test decorator * Remove optional extra asterisks * Revert return_dict changes in PT code * Update src/transformers/models/sam/modeling_tf_sam.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Remove None return annotations on init methods * Update tests/models/sam/test_processor_sam.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Fix input_boxes shapes * make fixup --------- Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
107 lines
4.7 KiB
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107 lines
4.7 KiB
Plaintext
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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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# SAM
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## Overview
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SAM (Segment Anything Model) was proposed in [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf) by Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.
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The model can be used to predict segmentation masks of any object of interest given an input image.
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The abstract from the paper is the following:
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*We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensed and privacy respecting images. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks. We evaluate its capabilities on numerous tasks and find that its zero-shot performance is impressive -- often competitive with or even superior to prior fully supervised results. We are releasing the Segment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and 11M images at [https://segment-anything.com](https://segment-anything.com) to foster research into foundation models for computer vision.*
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Tips:
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- The model predicts binary masks that states the presence or not of the object of interest given an image.
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- The model predicts much better results if input 2D points and/or input bounding boxes are provided
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- You can prompt multiple points for the same image, and predict a single mask.
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- Fine-tuning the model is not supported yet
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- According to the paper, textual input should be also supported. However, at this time of writing this seems to be not supported according to [the official repository](https://github.com/facebookresearch/segment-anything/issues/4#issuecomment-1497626844).
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This model was contributed by [ybelkada](https://huggingface.co/ybelkada) and [ArthurZ](https://huggingface.co/ArthurZ).
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The original code can be found [here](https://github.com/facebookresearch/segment-anything).
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Below is an example on how to run mask generation given an image and a 2D point:
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```python
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import torch
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from PIL import Image
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import requests
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from transformers import SamModel, SamProcessor
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model = SamModel.from_pretrained("facebook/sam-vit-huge").to(device)
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processor = SamProcessor.from_pretrained("facebook/sam-vit-huge")
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img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
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raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
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input_points = [[[450, 600]]] # 2D location of a window in the image
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inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to(device)
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outputs = model(**inputs)
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masks = processor.image_processor.post_process_masks(
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outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu()
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)
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scores = outputs.iou_scores
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```
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Resources:
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- [Demo notebook](https://github.com/huggingface/notebooks/blob/main/examples/segment_anything.ipynb) for using the model.
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- [Demo notebook](https://github.com/huggingface/notebooks/blob/main/examples/automatic_mask_generation.ipynb) for using the automatic mask generation pipeline.
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- [Demo notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/SAM/Run_inference_with_MedSAM_using_HuggingFace_Transformers.ipynb) for inference with MedSAM, a fine-tuned version of SAM on the medical domain.
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- [Demo notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/SAM/Fine_tune_SAM_(segment_anything)_on_a_custom_dataset.ipynb) for fine-tuning the model on custom data.
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## SamConfig
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[[autodoc]] SamConfig
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## SamVisionConfig
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[[autodoc]] SamVisionConfig
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## SamMaskDecoderConfig
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[[autodoc]] SamMaskDecoderConfig
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## SamPromptEncoderConfig
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[[autodoc]] SamPromptEncoderConfig
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## SamProcessor
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[[autodoc]] SamProcessor
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## SamImageProcessor
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[[autodoc]] SamImageProcessor
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## SamModel
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[[autodoc]] SamModel
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- forward
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## TFSamModel
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[[autodoc]] TFSamModel
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- call |