`ultralytics 8.0.134` add MobileSAM support (#3474)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com> Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com> Co-authored-by: Laughing-q <1185102784@qq.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>single_channel
Chaoning Zhang
1 year ago
committed by
GitHub
parent
c55a98ab8e
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201e69e4e4
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---
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comments: true
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description: MobileSAM is a lightweight adaptation of the Segment Anything Model (SAM) designed for mobile applications. It maintains the full functionality of the original SAM while significantly improving speed, making it suitable for CPU-only edge devices, such as mobile phones.
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keywords: MobileSAM, Faster Segment Anything, Segment Anything, Segment Anything Model, SAM, Meta SAM, image segmentation, promptable segmentation, zero-shot performance, SA-1B dataset, advanced architecture, auto-annotation, Ultralytics, pre-trained models, SAM base, SAM large, instance segmentation, computer vision, AI, artificial intelligence, machine learning, data annotation, segmentation masks, detection model, YOLO detection model, bibtex, Meta AI
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---
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# Faster Segment Anything (MobileSAM)
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The MobileSAM paper is now available on [ResearchGate](https://www.researchgate.net/publication/371851844_Faster_Segment_Anything_Towards_Lightweight_SAM_for_Mobile_Applications) and [arXiv](https://arxiv.org/pdf/2306.14289.pdf). The most recent version will initially appear on ResearchGate due to the delayed content update on arXiv.
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A demonstration of MobileSAM running on a CPU can be accessed at this [demo link](https://huggingface.co/spaces/dhkim2810/MobileSAM). The performance on a Mac i5 CPU takes approximately 3 seconds. On the Hugging Face demo, the interface and lower-performance CPUs contribute to a slower response, but it continues to function effectively.
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MobileSAM is implemented in various projects including [Grounding-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything), [AnyLabeling](https://github.com/vietanhdev/anylabeling), and [SegmentAnythingin3D](https://github.com/Jumpat/SegmentAnythingin3D).
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MobileSAM is trained on a single GPU with a 100k dataset (1% of the original images) in less than a day. The code for this training will be made available in the future.
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## Adapting from SAM to MobileSAM
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Since MobileSAM retains the same pipeline as the original SAM, we have incorporated the original's pre-processing, post-processing, and all other interfaces. Consequently, those currently using the original SAM can transition to MobileSAM with minimal effort.
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MobileSAM performs comparably to the original SAM and retains the same pipeline except for a change in the image encoder. Specifically, we replace the original heavyweight ViT-H encoder (632M) with a smaller Tiny-ViT (5M). On a single GPU, MobileSAM operates at about 12ms per image: 8ms on the image encoder and 4ms on the mask decoder.
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The following table provides a comparison of ViT-based image encoders:
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| Image Encoder | Original SAM | MobileSAM |
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|---------------|--------------|-----------|
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| Parameters | 611M | 5M |
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| Speed | 452ms | 8ms |
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Both the original SAM and MobileSAM utilize the same prompt-guided mask decoder:
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| Mask Decoder | Original SAM | MobileSAM |
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|--------------|--------------|-----------|
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| Parameters | 3.876M | 3.876M |
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| Speed | 4ms | 4ms |
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Here is the comparison of the whole pipeline:
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| Whole Pipeline (Enc+Dec) | Original SAM | MobileSAM |
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|--------------------------|--------------|-----------|
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| Parameters | 615M | 9.66M |
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| Speed | 456ms | 12ms |
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The performance of MobileSAM and the original SAM are demonstrated using both a point and a box as prompts.
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With its superior performance, MobileSAM is approximately 5 times smaller and 7 times faster than the current FastSAM. More details are available at the [MobileSAM project page](https://github.com/ChaoningZhang/MobileSAM).
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## Testing MobileSAM in Ultralytics
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Just like the original SAM, we offer a straightforward testing method in Ultralytics, including modes for both Point and Box prompts.
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### Model Download
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You can download the model [here](https://github.com/ChaoningZhang/MobileSAM/blob/master/weights/mobile_sam.pt).
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### Point Prompt
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```python
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from ultralytics import SAM
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# Load the model
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model = SAM('mobile_sam.pt')
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# Predict a segment based on a point prompt
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model.predict('ultralytics/assets/zidane.jpg', points=[900, 370], labels=[1])
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```
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### Box Prompt
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```python
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from ultralytics import SAM
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# Load the model
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model = SAM('mobile_sam.pt')
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# Predict a segment based on a box prompt
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model.predict('ultralytics/assets/zidane.jpg', bboxes=[439, 437, 524, 709])
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```
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We have implemented `MobileSAM` and `SAM` using the same API. For more usage information, please see the [SAM page](./sam.md).
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### Citing MobileSAM
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If you find MobileSAM useful in your research or development work, please consider citing our paper:
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```bibtex
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@article{mobile_sam,
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title={Faster Segment Anything: Towards Lightweight SAM for Mobile Applications},
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author={Zhang, Chaoning and Han, Dongshen and Qiao, Yu and Kim, Jung Uk and Bae, Sung Ho and Lee, Seungkyu and Hong, Choong Seon},
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journal={arXiv preprint arXiv:2306.14289},
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year={2023}
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}
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```
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---
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description: Learn how to use the ResizeLongestSide module in Ultralytics YOLO for automatic image resizing. Resize your images with ease.
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keywords: ResizeLongestSide, Ultralytics YOLO, automatic image resizing, image resizing
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---
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## ResizeLongestSide
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---
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### ::: ultralytics.vit.sam.autosize.ResizeLongestSide
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<br><br>
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---
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description: Learn about the SamAutomaticMaskGenerator module in Ultralytics YOLO, an automatic mask generator for image segmentation.
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keywords: SamAutomaticMaskGenerator, Ultralytics YOLO, automatic mask generator, image segmentation
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---
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## SamAutomaticMaskGenerator
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---
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### ::: ultralytics.vit.sam.modules.mask_generator.SamAutomaticMaskGenerator
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<br><br>
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---
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description: Learn about PromptPredictor - a module in Ultralytics VIT SAM that predicts image captions based on prompts. Get started today!.
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keywords: PromptPredictor, Ultralytics, YOLO, VIT SAM, image captioning, deep learning, computer vision
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---
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## PromptPredictor
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---
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### ::: ultralytics.vit.sam.modules.prompt_predictor.PromptPredictor
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<br><br>
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---
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description: Learn about the Conv2d_BN, MBConv, ConvLayer, Attention, BasicLayer, and TinyViT modules.
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keywords: Conv2d_BN, MBConv, ConvLayer, Attention, BasicLayer, TinyViT
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---
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## Conv2d_BN
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---
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### ::: ultralytics.vit.sam.modules.tiny_encoder.Conv2d_BN
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<br><br>
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## PatchEmbed
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---
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### ::: ultralytics.vit.sam.modules.tiny_encoder.PatchEmbed
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<br><br>
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## MBConv
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---
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### ::: ultralytics.vit.sam.modules.tiny_encoder.MBConv
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<br><br>
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## PatchMerging
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---
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### ::: ultralytics.vit.sam.modules.tiny_encoder.PatchMerging
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<br><br>
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## ConvLayer
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---
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### ::: ultralytics.vit.sam.modules.tiny_encoder.ConvLayer
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<br><br>
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## Mlp
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---
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### ::: ultralytics.vit.sam.modules.tiny_encoder.Mlp
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<br><br>
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## Attention
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---
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### ::: ultralytics.vit.sam.modules.tiny_encoder.Attention
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<br><br>
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## TinyViTBlock
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---
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### ::: ultralytics.vit.sam.modules.tiny_encoder.TinyViTBlock
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<br><br>
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## BasicLayer
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---
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### ::: ultralytics.vit.sam.modules.tiny_encoder.BasicLayer
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<br><br>
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## LayerNorm2d
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---
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### ::: ultralytics.vit.sam.modules.tiny_encoder.LayerNorm2d
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<br><br>
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## TinyViT
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---
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### ::: ultralytics.vit.sam.modules.tiny_encoder.TinyViT
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<br><br>
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---
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description: Learn how to use FastSAM in Ultralytics YOLO to improve object detection accuracy and speed.
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keywords: FastSAM, object detection, accuracy, speed, Ultralytics YOLO
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---
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## FastSAM
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---
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### ::: ultralytics.yolo.fastsam.model.FastSAM
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<br><br>
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---
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description: FastSAMPredictor API reference and usage guide for the Ultralytics YOLO object detection library.
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keywords: FastSAMPredictor, API, reference, usage, guide, Ultralytics, YOLO, object detection, library
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---
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## FastSAMPredictor
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---
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### ::: ultralytics.yolo.fastsam.predict.FastSAMPredictor
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<br><br>
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---
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description: Learn how to use FastSAMPrompt in Ultralytics YOLO for fast and efficient object detection and tracking.
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keywords: FastSAMPrompt, Ultralytics YOLO, object detection, tracking, fast, efficient
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---
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## FastSAMPrompt
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---
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### ::: ultralytics.yolo.fastsam.prompt.FastSAMPrompt
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<br><br>
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---
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description: Learn how to adjust bounding boxes to the image border in Ultralytics YOLO framework. Improve object detection accuracy by accounting for image borders.
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keywords: adjust_bboxes_to_image_border, Ultralytics YOLO, object detection, bounding boxes, image border
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---
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## adjust_bboxes_to_image_border
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---
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### ::: ultralytics.yolo.fastsam.utils.adjust_bboxes_to_image_border
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<br><br>
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## bbox_iou
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---
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### ::: ultralytics.yolo.fastsam.utils.bbox_iou
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<br><br>
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---
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description: Learn about the FastSAMValidator module in Ultralytics YOLO. Validate and evaluate Segment Anything Model (SAM) datasets for object detection models with ease.
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keywords: FastSAMValidator, Ultralytics YOLO, SAM datasets, object detection, validation, evaluation
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---
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## FastSAMValidator
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---
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### ::: ultralytics.yolo.fastsam.val.FastSAMValidator
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<br><br>
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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from .build import build_sam # noqa
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from .model import SAM # noqa
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from .modules.prompt_predictor import PromptPredictor # noqa
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from .model import SAM
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from .predict import Predictor
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# from .build import build_sam
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__all__ = 'SAM', 'Predictor' # tuple or list
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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from copy import deepcopy
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from typing import Tuple
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import numpy as np
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import torch
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from torch.nn import functional as F
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from torchvision.transforms.functional import resize, to_pil_image # type: ignore
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class ResizeLongestSide:
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"""
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Resizes images to the longest side 'target_length', as well as provides
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methods for resizing coordinates and boxes. Provides methods for
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transforming both numpy array and batched torch tensors.
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"""
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def __init__(self, target_length: int) -> None:
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self.target_length = target_length
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def apply_image(self, image: np.ndarray) -> np.ndarray:
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"""
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Expects a numpy array with shape HxWxC in uint8 format.
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"""
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target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)
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return np.array(resize(to_pil_image(image), target_size))
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def apply_coords(self, coords: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
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"""
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Expects a numpy array of length 2 in the final dimension. Requires the
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original image size in (H, W) format.
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"""
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old_h, old_w = original_size
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new_h, new_w = self.get_preprocess_shape(original_size[0], original_size[1], self.target_length)
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coords = deepcopy(coords).astype(float)
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coords[..., 0] = coords[..., 0] * (new_w / old_w)
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coords[..., 1] = coords[..., 1] * (new_h / old_h)
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return coords
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def apply_boxes(self, boxes: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
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"""
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Expects a numpy array shape Bx4. Requires the original image size
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in (H, W) format.
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"""
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boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size)
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return boxes.reshape(-1, 4)
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def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor:
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"""
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Expects batched images with shape BxCxHxW and float format. This
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transformation may not exactly match apply_image. apply_image is
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the transformation expected by the model.
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"""
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# Expects an image in BCHW format. May not exactly match apply_image.
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target_size = self.get_preprocess_shape(image.shape[2], image.shape[3], self.target_length)
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return F.interpolate(image, target_size, mode='bilinear', align_corners=False, antialias=True)
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def apply_coords_torch(self, coords: torch.Tensor, original_size: Tuple[int, ...]) -> torch.Tensor:
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"""
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Expects a torch tensor with length 2 in the last dimension. Requires the
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original image size in (H, W) format.
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"""
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old_h, old_w = original_size
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new_h, new_w = self.get_preprocess_shape(original_size[0], original_size[1], self.target_length)
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coords = deepcopy(coords).to(torch.float)
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coords[..., 0] = coords[..., 0] * (new_w / old_w)
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coords[..., 1] = coords[..., 1] * (new_h / old_h)
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return coords
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def apply_boxes_torch(self, boxes: torch.Tensor, original_size: Tuple[int, ...]) -> torch.Tensor:
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"""
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Expects a torch tensor with shape Bx4. Requires the original image
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size in (H, W) format.
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"""
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boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size)
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return boxes.reshape(-1, 4)
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@staticmethod
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def get_preprocess_shape(oldh: int, oldw: int, long_side_length: int) -> Tuple[int, int]:
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"""
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Compute the output size given input size and target long side length.
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"""
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scale = long_side_length * 1.0 / max(oldh, oldw)
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newh, neww = oldh * scale, oldw * scale
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neww = int(neww + 0.5)
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newh = int(newh + 0.5)
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return (newh, neww)
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@ -1,353 +0,0 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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from typing import Any, Dict, List, Optional, Tuple
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import numpy as np
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import torch
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from torchvision.ops.boxes import batched_nms, box_area # type: ignore
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from ..amg import (MaskData, area_from_rle, batch_iterator, batched_mask_to_box, box_xyxy_to_xywh,
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build_all_layer_point_grids, calculate_stability_score, coco_encode_rle, generate_crop_boxes,
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is_box_near_crop_edge, mask_to_rle_pytorch, remove_small_regions, rle_to_mask, uncrop_boxes_xyxy,
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uncrop_masks, uncrop_points)
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from .prompt_predictor import PromptPredictor
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from .sam import Sam
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class SamAutomaticMaskGenerator:
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def __init__(
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self,
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model: Sam,
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points_per_side: Optional[int] = 32,
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points_per_batch: int = 64,
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pred_iou_thresh: float = 0.88,
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stability_score_thresh: float = 0.95,
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stability_score_offset: float = 1.0,
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box_nms_thresh: float = 0.7,
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crop_n_layers: int = 0,
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crop_nms_thresh: float = 0.7,
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crop_overlap_ratio: float = 512 / 1500,
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crop_n_points_downscale_factor: int = 1,
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point_grids: Optional[List[np.ndarray]] = None,
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min_mask_region_area: int = 0,
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output_mode: str = 'binary_mask',
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) -> None:
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"""
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Using a SAM model, generates masks for the entire image.
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Generates a grid of point prompts over the image, then filters
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low quality and duplicate masks. The default settings are chosen
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for SAM with a ViT-H backbone.
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Arguments:
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model (Sam): The SAM model to use for mask prediction.
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points_per_side (int, None): The number of points to be sampled
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along one side of the image. The total number of points is
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points_per_side**2. If None, 'point_grids' must provide explicit
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point sampling.
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points_per_batch (int): Sets the number of points run simultaneously
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by the model. Higher numbers may be faster but use more GPU memory.
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pred_iou_thresh (float): A filtering threshold in [0,1], using the
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model's predicted mask quality.
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stability_score_thresh (float): A filtering threshold in [0,1], using
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the stability of the mask under changes to the cutoff used to binarize
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the model's mask predictions.
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stability_score_offset (float): The amount to shift the cutoff when
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calculated the stability score.
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box_nms_thresh (float): The box IoU cutoff used by non-maximal
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suppression to filter duplicate masks.
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crop_n_layers (int): If >0, mask prediction will be run again on
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crops of the image. Sets the number of layers to run, where each
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layer has 2**i_layer number of image crops.
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crop_nms_thresh (float): The box IoU cutoff used by non-maximal
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suppression to filter duplicate masks between different crops.
|
||||
crop_overlap_ratio (float): Sets the degree to which crops overlap.
|
||||
In the first crop layer, crops will overlap by this fraction of
|
||||
the image length. Later layers with more crops scale down this overlap.
|
||||
crop_n_points_downscale_factor (int): The number of points-per-side
|
||||
sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
|
||||
point_grids (list(np.ndarray), None): A list over explicit grids
|
||||
of points used for sampling, normalized to [0,1]. The nth grid in the
|
||||
list is used in the nth crop layer. Exclusive with points_per_side.
|
||||
min_mask_region_area (int): If >0, postprocessing will be applied
|
||||
to remove disconnected regions and holes in masks with area smaller
|
||||
than min_mask_region_area. Requires opencv.
|
||||
output_mode (str): The form masks are returned in. Can be 'binary_mask',
|
||||
'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.
|
||||
For large resolutions, 'binary_mask' may consume large amounts of
|
||||
memory.
|
||||
"""
|
||||
|
||||
assert (points_per_side is None) != (point_grids is None), \
|
||||
'Exactly one of points_per_side or point_grid must be provided.'
|
||||
if points_per_side is not None:
|
||||
self.point_grids = build_all_layer_point_grids(
|
||||
points_per_side,
|
||||
crop_n_layers,
|
||||
crop_n_points_downscale_factor,
|
||||
)
|
||||
elif point_grids is not None:
|
||||
self.point_grids = point_grids
|
||||
else:
|
||||
raise ValueError("Can't have both points_per_side and point_grid be None.")
|
||||
|
||||
assert output_mode in {'binary_mask', 'uncompressed_rle', 'coco_rle'}, f'Unknown output_mode {output_mode}.'
|
||||
if output_mode == 'coco_rle':
|
||||
from pycocotools import mask as mask_utils # type: ignore # noqa: F401
|
||||
|
||||
if min_mask_region_area > 0:
|
||||
import cv2 # type: ignore # noqa: F401
|
||||
|
||||
self.predictor = PromptPredictor(model)
|
||||
self.points_per_batch = points_per_batch
|
||||
self.pred_iou_thresh = pred_iou_thresh
|
||||
self.stability_score_thresh = stability_score_thresh
|
||||
self.stability_score_offset = stability_score_offset
|
||||
self.box_nms_thresh = box_nms_thresh
|
||||
self.crop_n_layers = crop_n_layers
|
||||
self.crop_nms_thresh = crop_nms_thresh
|
||||
self.crop_overlap_ratio = crop_overlap_ratio
|
||||
self.crop_n_points_downscale_factor = crop_n_points_downscale_factor
|
||||
self.min_mask_region_area = min_mask_region_area
|
||||
self.output_mode = output_mode
|
||||
|
||||
# TODO: Temporary implementation for compatibility
|
||||
def __call__(self, image: np.ndarray, augment=False, visualize=False) -> List[Dict[str, Any]]:
|
||||
return self.generate(image)
|
||||
|
||||
@torch.no_grad()
|
||||
def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Generates masks for the given image.
|
||||
|
||||
Arguments:
|
||||
image (np.ndarray): The image to generate masks for, in HWC uint8 format.
|
||||
|
||||
Returns:
|
||||
list(dict(str, any)): A list over records for masks. Each record is a dict containing the following keys:
|
||||
segmentation (dict(str, any), np.ndarray): The mask. If
|
||||
output_mode='binary_mask', is an array of shape HW. Otherwise,
|
||||
is a dictionary containing the RLE.
|
||||
bbox (list(float)): The box around the mask, in XYWH format.
|
||||
area (int): The area in pixels of the mask.
|
||||
predicted_iou (float): The model's own prediction of the mask's
|
||||
quality. This is filtered by the pred_iou_thresh parameter.
|
||||
point_coords (list(list(float))): The point coordinates input
|
||||
to the model to generate this mask.
|
||||
stability_score (float): A measure of the mask's quality. This
|
||||
is filtered on using the stability_score_thresh parameter.
|
||||
crop_box (list(float)): The crop of the image used to generate
|
||||
the mask, given in XYWH format.
|
||||
"""
|
||||
|
||||
# Generate masks
|
||||
mask_data = self._generate_masks(image)
|
||||
|
||||
# Filter small disconnected regions and holes in masks
|
||||
if self.min_mask_region_area > 0:
|
||||
mask_data = self.postprocess_small_regions(
|
||||
mask_data,
|
||||
self.min_mask_region_area,
|
||||
max(self.box_nms_thresh, self.crop_nms_thresh),
|
||||
)
|
||||
|
||||
# Encode masks
|
||||
if self.output_mode == 'coco_rle':
|
||||
mask_data['segmentations'] = [coco_encode_rle(rle) for rle in mask_data['rles']]
|
||||
elif self.output_mode == 'binary_mask':
|
||||
mask_data['segmentations'] = [rle_to_mask(rle) for rle in mask_data['rles']]
|
||||
else:
|
||||
mask_data['segmentations'] = mask_data['rles']
|
||||
|
||||
# Write mask records
|
||||
curr_anns = []
|
||||
for idx in range(len(mask_data['segmentations'])):
|
||||
ann = {
|
||||
'segmentation': mask_data['segmentations'][idx],
|
||||
'area': area_from_rle(mask_data['rles'][idx]),
|
||||
'bbox': box_xyxy_to_xywh(mask_data['boxes'][idx]).tolist(),
|
||||
'predicted_iou': mask_data['iou_preds'][idx].item(),
|
||||
'point_coords': [mask_data['points'][idx].tolist()],
|
||||
'stability_score': mask_data['stability_score'][idx].item(),
|
||||
'crop_box': box_xyxy_to_xywh(mask_data['crop_boxes'][idx]).tolist(), }
|
||||
curr_anns.append(ann)
|
||||
|
||||
return curr_anns
|
||||
|
||||
def _generate_masks(self, image: np.ndarray) -> MaskData:
|
||||
orig_size = image.shape[:2]
|
||||
crop_boxes, layer_idxs = generate_crop_boxes(orig_size, self.crop_n_layers, self.crop_overlap_ratio)
|
||||
|
||||
# Iterate over image crops
|
||||
data = MaskData()
|
||||
for crop_box, layer_idx in zip(crop_boxes, layer_idxs):
|
||||
crop_data = self._process_crop(image, crop_box, layer_idx, orig_size)
|
||||
data.cat(crop_data)
|
||||
|
||||
# Remove duplicate masks between crops
|
||||
if len(crop_boxes) > 1:
|
||||
# Prefer masks from smaller crops
|
||||
scores = 1 / box_area(data['crop_boxes'])
|
||||
scores = scores.to(data['boxes'].device)
|
||||
keep_by_nms = batched_nms(
|
||||
data['boxes'].float(),
|
||||
scores,
|
||||
torch.zeros_like(data['boxes'][:, 0]), # categories
|
||||
iou_threshold=self.crop_nms_thresh,
|
||||
)
|
||||
data.filter(keep_by_nms)
|
||||
|
||||
data.to_numpy()
|
||||
return data
|
||||
|
||||
def _process_crop(
|
||||
self,
|
||||
image: np.ndarray,
|
||||
crop_box: List[int],
|
||||
crop_layer_idx: int,
|
||||
orig_size: Tuple[int, ...],
|
||||
) -> MaskData:
|
||||
# Crop the image and calculate embeddings
|
||||
x0, y0, x1, y1 = crop_box
|
||||
cropped_im = image[y0:y1, x0:x1, :]
|
||||
cropped_im_size = cropped_im.shape[:2]
|
||||
self.predictor.set_image(cropped_im)
|
||||
|
||||
# Get points for this crop
|
||||
points_scale = np.array(cropped_im_size)[None, ::-1]
|
||||
points_for_image = self.point_grids[crop_layer_idx] * points_scale
|
||||
|
||||
# Generate masks for this crop in batches
|
||||
data = MaskData()
|
||||
for (points, ) in batch_iterator(self.points_per_batch, points_for_image):
|
||||
batch_data = self._process_batch(points, cropped_im_size, crop_box, orig_size)
|
||||
data.cat(batch_data)
|
||||
del batch_data
|
||||
self.predictor.reset_image()
|
||||
|
||||
# Remove duplicates within this crop.
|
||||
keep_by_nms = batched_nms(
|
||||
data['boxes'].float(),
|
||||
data['iou_preds'],
|
||||
torch.zeros_like(data['boxes'][:, 0]), # categories
|
||||
iou_threshold=self.box_nms_thresh,
|
||||
)
|
||||
data.filter(keep_by_nms)
|
||||
|
||||
# Return to the original image frame
|
||||
data['boxes'] = uncrop_boxes_xyxy(data['boxes'], crop_box)
|
||||
data['points'] = uncrop_points(data['points'], crop_box)
|
||||
data['crop_boxes'] = torch.tensor([crop_box for _ in range(len(data['rles']))])
|
||||
|
||||
return data
|
||||
|
||||
def _process_batch(
|
||||
self,
|
||||
points: np.ndarray,
|
||||
im_size: Tuple[int, ...],
|
||||
crop_box: List[int],
|
||||
orig_size: Tuple[int, ...],
|
||||
) -> MaskData:
|
||||
orig_h, orig_w = orig_size
|
||||
|
||||
# Run model on this batch
|
||||
transformed_points = self.predictor.transform.apply_coords(points, im_size)
|
||||
in_points = torch.as_tensor(transformed_points, device=self.predictor.device)
|
||||
in_labels = torch.ones(in_points.shape[0], dtype=torch.int, device=in_points.device)
|
||||
masks, iou_preds, _ = self.predictor.predict_torch(
|
||||
in_points[:, None, :],
|
||||
in_labels[:, None],
|
||||
multimask_output=True,
|
||||
return_logits=True,
|
||||
)
|
||||
|
||||
# Serialize predictions and store in MaskData
|
||||
data = MaskData(
|
||||
masks=masks.flatten(0, 1),
|
||||
iou_preds=iou_preds.flatten(0, 1),
|
||||
points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)),
|
||||
)
|
||||
del masks
|
||||
|
||||
# Filter by predicted IoU
|
||||
if self.pred_iou_thresh > 0.0:
|
||||
keep_mask = data['iou_preds'] > self.pred_iou_thresh
|
||||
data.filter(keep_mask)
|
||||
|
||||
# Calculate stability score
|
||||
data['stability_score'] = calculate_stability_score(data['masks'], self.predictor.model.mask_threshold,
|
||||
self.stability_score_offset)
|
||||
if self.stability_score_thresh > 0.0:
|
||||
keep_mask = data['stability_score'] >= self.stability_score_thresh
|
||||
data.filter(keep_mask)
|
||||
|
||||
# Threshold masks and calculate boxes
|
||||
data['masks'] = data['masks'] > self.predictor.model.mask_threshold
|
||||
data['boxes'] = batched_mask_to_box(data['masks'])
|
||||
|
||||
# Filter boxes that touch crop boundaries
|
||||
keep_mask = ~is_box_near_crop_edge(data['boxes'], crop_box, [0, 0, orig_w, orig_h])
|
||||
if not torch.all(keep_mask):
|
||||
data.filter(keep_mask)
|
||||
|
||||
# Compress to RLE
|
||||
data['masks'] = uncrop_masks(data['masks'], crop_box, orig_h, orig_w)
|
||||
data['rles'] = mask_to_rle_pytorch(data['masks'])
|
||||
del data['masks']
|
||||
|
||||
return data
|
||||
|
||||
@staticmethod
|
||||
def postprocess_small_regions(mask_data: MaskData, min_area: int, nms_thresh: float) -> MaskData:
|
||||
"""
|
||||
Removes small disconnected regions and holes in masks, then reruns
|
||||
box NMS to remove any new duplicates.
|
||||
|
||||
Edits mask_data in place.
|
||||
|
||||
Requires open-cv as a dependency.
|
||||
"""
|
||||
if len(mask_data['rles']) == 0:
|
||||
return mask_data
|
||||
|
||||
# Filter small disconnected regions and holes
|
||||
new_masks = []
|
||||
scores = []
|
||||
for rle in mask_data['rles']:
|
||||
mask = rle_to_mask(rle)
|
||||
|
||||
mask, changed = remove_small_regions(mask, min_area, mode='holes')
|
||||
unchanged = not changed
|
||||
mask, changed = remove_small_regions(mask, min_area, mode='islands')
|
||||
unchanged = unchanged and not changed
|
||||
|
||||
new_masks.append(torch.as_tensor(mask).unsqueeze(0))
|
||||
# Give score=0 to changed masks and score=1 to unchanged masks
|
||||
# so NMS will prefer ones that didn't need postprocessing
|
||||
scores.append(float(unchanged))
|
||||
|
||||
# Recalculate boxes and remove any new duplicates
|
||||
masks = torch.cat(new_masks, dim=0)
|
||||
boxes = batched_mask_to_box(masks)
|
||||
keep_by_nms = batched_nms(
|
||||
boxes.float(),
|
||||
torch.as_tensor(scores),
|
||||
torch.zeros_like(boxes[:, 0]), # categories
|
||||
iou_threshold=nms_thresh,
|
||||
)
|
||||
|
||||
# Only recalculate RLEs for masks that have changed
|
||||
for i_mask in keep_by_nms:
|
||||
if scores[i_mask] == 0.0:
|
||||
mask_torch = masks[i_mask].unsqueeze(0)
|
||||
mask_data['rles'][i_mask] = mask_to_rle_pytorch(mask_torch)[0]
|
||||
mask_data['boxes'][i_mask] = boxes[i_mask] # update res directly
|
||||
mask_data.filter(keep_by_nms)
|
||||
|
||||
return mask_data
|
||||
@ -1,242 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
|
||||
from typing import Optional, Tuple
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
from ..autosize import ResizeLongestSide
|
||||
from .sam import Sam
|
||||
|
||||
|
||||
class PromptPredictor:
|
||||
|
||||
def __init__(self, sam_model: Sam) -> None:
|
||||
"""
|
||||
Uses SAM to calculate the image embedding for an image, and then
|
||||
allow repeated, efficient mask prediction given prompts.
|
||||
|
||||
Arguments:
|
||||
sam_model (Sam): The model to use for mask prediction.
|
||||
"""
|
||||
super().__init__()
|
||||
self.model = sam_model
|
||||
self.transform = ResizeLongestSide(sam_model.image_encoder.img_size)
|
||||
self.reset_image()
|
||||
|
||||
def set_image(self, image: np.ndarray, image_format: str = 'RGB') -> None:
|
||||
"""
|
||||
Calculates the image embeddings for the provided image, allowing
|
||||
masks to be predicted with the 'predict' method.
|
||||
|
||||
Arguments:
|
||||
image (np.ndarray): The image for calculating masks. Expects an
|
||||
image in HWC uint8 format, with pixel values in [0, 255].
|
||||
image_format (str): The color format of the image, in ['RGB', 'BGR'].
|
||||
"""
|
||||
assert image_format in {'RGB', 'BGR'}, f"image_format must be in ['RGB', 'BGR'], is {image_format}."
|
||||
if image_format != self.model.image_format:
|
||||
image = image[..., ::-1]
|
||||
|
||||
# Transform the image to the form expected by the model
|
||||
input_image = self.transform.apply_image(image)
|
||||
input_image_torch = torch.as_tensor(input_image, device=self.device)
|
||||
input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :]
|
||||
|
||||
self.set_torch_image(input_image_torch, image.shape[:2])
|
||||
|
||||
@torch.no_grad()
|
||||
def set_torch_image(self, transformed_image: torch.Tensor, original_image_size: Tuple[int, ...]) -> None:
|
||||
"""
|
||||
Calculates the image embeddings for the provided image, allowing
|
||||
masks to be predicted with the 'predict' method. Expects the input
|
||||
image to be already transformed to the format expected by the model.
|
||||
|
||||
Arguments:
|
||||
transformed_image (torch.Tensor): The input image, with shape
|
||||
1x3xHxW, which has been transformed with ResizeLongestSide.
|
||||
original_image_size (tuple(int, int)): The size of the image
|
||||
before transformation, in (H, W) format.
|
||||
"""
|
||||
if len(transformed_image.shape) != 4 \
|
||||
or transformed_image.shape[1] != 3 \
|
||||
or max(*transformed_image.shape[2:]) != self.model.image_encoder.img_size:
|
||||
raise ValueError('set_torch_image input must be BCHW with long side {self.model.image_encoder.img_size}.')
|
||||
self.reset_image()
|
||||
|
||||
self.original_size = original_image_size
|
||||
self.input_size = tuple(transformed_image.shape[-2:])
|
||||
input_image = self.model.preprocess(transformed_image)
|
||||
self.features = self.model.image_encoder(input_image)
|
||||
self.is_image_set = True
|
||||
|
||||
def predict(
|
||||
self,
|
||||
point_coords: Optional[np.ndarray] = None,
|
||||
point_labels: Optional[np.ndarray] = None,
|
||||
box: Optional[np.ndarray] = None,
|
||||
mask_input: Optional[np.ndarray] = None,
|
||||
multimask_output: bool = True,
|
||||
return_logits: bool = False,
|
||||
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
|
||||
"""
|
||||
Predict masks for the given input prompts, using the currently set image.
|
||||
|
||||
Arguments:
|
||||
point_coords (np.ndarray, None): A Nx2 array of point prompts to the
|
||||
model. Each point is in (X,Y) in pixels.
|
||||
point_labels (np.ndarray, None): A length N array of labels for the
|
||||
point prompts. 1 indicates a foreground point and 0 indicates a
|
||||
background point.
|
||||
box (np.ndarray, None): A length 4 array given a box prompt to the
|
||||
model, in XYXY format.
|
||||
mask_input (np.ndarray): A low resolution mask input to the model, typically
|
||||
coming from a previous prediction iteration. Has form 1xHxW, where
|
||||
for SAM, H=W=256.
|
||||
multimask_output (bool): If true, the model will return three masks.
|
||||
For ambiguous input prompts (such as a single click), this will often
|
||||
produce better masks than a single prediction. If only a single
|
||||
mask is needed, the model's predicted quality score can be used
|
||||
to select the best mask. For non-ambiguous prompts, such as multiple
|
||||
input prompts, multimask_output=False can give better results.
|
||||
return_logits (bool): If true, returns un-thresholded masks logits
|
||||
instead of a binary mask.
|
||||
|
||||
Returns:
|
||||
(np.ndarray): The output masks in CxHxW format, where C is the
|
||||
number of masks, and (H, W) is the original image size.
|
||||
(np.ndarray): An array of length C containing the model's
|
||||
predictions for the quality of each mask.
|
||||
(np.ndarray): An array of shape CxHxW, where C is the number
|
||||
of masks and H=W=256. These low resolution logits can be passed to
|
||||
a subsequent iteration as mask input.
|
||||
"""
|
||||
if not self.is_image_set:
|
||||
raise RuntimeError('An image must be set with .set_image(...) before mask prediction.')
|
||||
|
||||
# Transform input prompts
|
||||
coords_torch, labels_torch, box_torch, mask_input_torch = None, None, None, None
|
||||
if point_coords is not None:
|
||||
assert (point_labels is not None), 'point_labels must be supplied if point_coords is supplied.'
|
||||
point_coords = self.transform.apply_coords(point_coords, self.original_size)
|
||||
coords_torch = torch.as_tensor(point_coords, dtype=torch.float, device=self.device)
|
||||
labels_torch = torch.as_tensor(point_labels, dtype=torch.int, device=self.device)
|
||||
coords_torch, labels_torch = coords_torch[None, :, :], labels_torch[None, :]
|
||||
if box is not None:
|
||||
box = self.transform.apply_boxes(box, self.original_size)
|
||||
box_torch = torch.as_tensor(box, dtype=torch.float, device=self.device)
|
||||
box_torch = box_torch[None, :]
|
||||
if mask_input is not None:
|
||||
mask_input_torch = torch.as_tensor(mask_input, dtype=torch.float, device=self.device)
|
||||
mask_input_torch = mask_input_torch[None, :, :, :]
|
||||
|
||||
masks, iou_predictions, low_res_masks = self.predict_torch(
|
||||
coords_torch,
|
||||
labels_torch,
|
||||
box_torch,
|
||||
mask_input_torch,
|
||||
multimask_output,
|
||||
return_logits=return_logits,
|
||||
)
|
||||
|
||||
masks_np = masks[0].detach().cpu().numpy()
|
||||
iou_predictions_np = iou_predictions[0].detach().cpu().numpy()
|
||||
low_res_masks_np = low_res_masks[0].detach().cpu().numpy()
|
||||
return masks_np, iou_predictions_np, low_res_masks_np
|
||||
|
||||
@torch.no_grad()
|
||||
def predict_torch(
|
||||
self,
|
||||
point_coords: Optional[torch.Tensor],
|
||||
point_labels: Optional[torch.Tensor],
|
||||
boxes: Optional[torch.Tensor] = None,
|
||||
mask_input: Optional[torch.Tensor] = None,
|
||||
multimask_output: bool = True,
|
||||
return_logits: bool = False,
|
||||
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
"""
|
||||
Predict masks for the given input prompts, using the currently set image.
|
||||
Input prompts are batched torch tensors and are expected to already be
|
||||
transformed to the input frame using ResizeLongestSide.
|
||||
|
||||
Arguments:
|
||||
point_coords (torch.Tensor, None): A BxNx2 array of point prompts to the
|
||||
model. Each point is in (X,Y) in pixels.
|
||||
point_labels (torch.Tensor, None): A BxN array of labels for the
|
||||
point prompts. 1 indicates a foreground point and 0 indicates a
|
||||
background point.
|
||||
boxes (np.ndarray, None): A Bx4 array given a box prompt to the
|
||||
model, in XYXY format.
|
||||
mask_input (np.ndarray): A low resolution mask input to the model, typically
|
||||
coming from a previous prediction iteration. Has form Bx1xHxW, where
|
||||
for SAM, H=W=256. Masks returned by a previous iteration of the
|
||||
predict method do not need further transformation.
|
||||
multimask_output (bool): If true, the model will return three masks.
|
||||
For ambiguous input prompts (such as a single click), this will often
|
||||
produce better masks than a single prediction. If only a single
|
||||
mask is needed, the model's predicted quality score can be used
|
||||
to select the best mask. For non-ambiguous prompts, such as multiple
|
||||
input prompts, multimask_output=False can give better results.
|
||||
return_logits (bool): If true, returns un-thresholded masks logits
|
||||
instead of a binary mask.
|
||||
|
||||
Returns:
|
||||
(torch.Tensor): The output masks in BxCxHxW format, where C is the
|
||||
number of masks, and (H, W) is the original image size.
|
||||
(torch.Tensor): An array of shape BxC containing the model's
|
||||
predictions for the quality of each mask.
|
||||
(torch.Tensor): An array of shape BxCxHxW, where C is the number
|
||||
of masks and H=W=256. These low res logits can be passed to
|
||||
a subsequent iteration as mask input.
|
||||
"""
|
||||
if not self.is_image_set:
|
||||
raise RuntimeError('An image must be set with .set_image(...) before mask prediction.')
|
||||
|
||||
points = (point_coords, point_labels) if point_coords is not None else None
|
||||
# Embed prompts
|
||||
sparse_embeddings, dense_embeddings = self.model.prompt_encoder(
|
||||
points=points,
|
||||
boxes=boxes,
|
||||
masks=mask_input,
|
||||
)
|
||||
|
||||
# Predict masks
|
||||
low_res_masks, iou_predictions = self.model.mask_decoder(
|
||||
image_embeddings=self.features,
|
||||
image_pe=self.model.prompt_encoder.get_dense_pe(),
|
||||
sparse_prompt_embeddings=sparse_embeddings,
|
||||
dense_prompt_embeddings=dense_embeddings,
|
||||
multimask_output=multimask_output,
|
||||
)
|
||||
|
||||
# Upscale the masks to the original image resolution
|
||||
masks = self.model.postprocess_masks(low_res_masks, self.input_size, self.original_size)
|
||||
|
||||
if not return_logits:
|
||||
masks = masks > self.model.mask_threshold
|
||||
|
||||
return masks, iou_predictions, low_res_masks
|
||||
|
||||
def get_image_embedding(self) -> torch.Tensor:
|
||||
"""
|
||||
Returns the image embeddings for the currently set image, with
|
||||
shape 1xCxHxW, where C is the embedding dimension and (H,W) are
|
||||
the embedding spatial dimension of SAM (typically C=256, H=W=64).
|
||||
"""
|
||||
if not self.is_image_set:
|
||||
raise RuntimeError('An image must be set with .set_image(...) to generate an embedding.')
|
||||
assert self.features is not None, 'Features must exist if an image has been set.'
|
||||
return self.features
|
||||
|
||||
@property
|
||||
def device(self) -> torch.device:
|
||||
return self.model.device
|
||||
|
||||
def reset_image(self) -> None:
|
||||
"""Resets the currently set image."""
|
||||
self.is_image_set = False
|
||||
self.features = None
|
||||
self.orig_h = None
|
||||
self.orig_w = None
|
||||
self.input_h = None
|
||||
self.input_w = None
|
||||
@ -0,0 +1,653 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
|
||||
# --------------------------------------------------------
|
||||
# TinyViT Model Architecture
|
||||
# Copyright (c) 2022 Microsoft
|
||||
# Adapted from LeViT and Swin Transformer
|
||||
# LeViT: (https://github.com/facebookresearch/levit)
|
||||
# Swin: (https://github.com/microsoft/swin-transformer)
|
||||
# Build the TinyViT Model
|
||||
# --------------------------------------------------------
|
||||
|
||||
import itertools
|
||||
from typing import Tuple
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torch.utils.checkpoint as checkpoint
|
||||
|
||||
from ultralytics.yolo.utils.instance import to_2tuple
|
||||
|
||||
|
||||
class Conv2d_BN(torch.nn.Sequential):
|
||||
|
||||
def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1, groups=1, bn_weight_init=1):
|
||||
super().__init__()
|
||||
self.add_module('c', torch.nn.Conv2d(a, b, ks, stride, pad, dilation, groups, bias=False))
|
||||
bn = torch.nn.BatchNorm2d(b)
|
||||
torch.nn.init.constant_(bn.weight, bn_weight_init)
|
||||
torch.nn.init.constant_(bn.bias, 0)
|
||||
self.add_module('bn', bn)
|
||||
|
||||
@torch.no_grad()
|
||||
def fuse(self):
|
||||
c, bn = self._modules.values()
|
||||
w = bn.weight / (bn.running_var + bn.eps) ** 0.5
|
||||
w = c.weight * w[:, None, None, None]
|
||||
b = bn.bias - bn.running_mean * bn.weight / \
|
||||
(bn.running_var + bn.eps)**0.5
|
||||
m = torch.nn.Conv2d(w.size(1) * self.c.groups,
|
||||
w.size(0),
|
||||
w.shape[2:],
|
||||
stride=self.c.stride,
|
||||
padding=self.c.padding,
|
||||
dilation=self.c.dilation,
|
||||
groups=self.c.groups)
|
||||
m.weight.data.copy_(w)
|
||||
m.bias.data.copy_(b)
|
||||
return m
|
||||
|
||||
|
||||
# NOTE: This module and timm package is needed only for training.
|
||||
# from ultralytics.yolo.utils.checks import check_requirements
|
||||
# check_requirements('timm')
|
||||
# from timm.models.layers import DropPath as TimmDropPath
|
||||
# from timm.models.layers import trunc_normal_
|
||||
# class DropPath(TimmDropPath):
|
||||
#
|
||||
# def __init__(self, drop_prob=None):
|
||||
# super().__init__(drop_prob=drop_prob)
|
||||
# self.drop_prob = drop_prob
|
||||
#
|
||||
# def __repr__(self):
|
||||
# msg = super().__repr__()
|
||||
# msg += f'(drop_prob={self.drop_prob})'
|
||||
# return msg
|
||||
|
||||
|
||||
class PatchEmbed(nn.Module):
|
||||
|
||||
def __init__(self, in_chans, embed_dim, resolution, activation):
|
||||
super().__init__()
|
||||
img_size: Tuple[int, int] = to_2tuple(resolution)
|
||||
self.patches_resolution = (img_size[0] // 4, img_size[1] // 4)
|
||||
self.num_patches = self.patches_resolution[0] * \
|
||||
self.patches_resolution[1]
|
||||
self.in_chans = in_chans
|
||||
self.embed_dim = embed_dim
|
||||
n = embed_dim
|
||||
self.seq = nn.Sequential(
|
||||
Conv2d_BN(in_chans, n // 2, 3, 2, 1),
|
||||
activation(),
|
||||
Conv2d_BN(n // 2, n, 3, 2, 1),
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
return self.seq(x)
|
||||
|
||||
|
||||
class MBConv(nn.Module):
|
||||
|
||||
def __init__(self, in_chans, out_chans, expand_ratio, activation, drop_path):
|
||||
super().__init__()
|
||||
self.in_chans = in_chans
|
||||
self.hidden_chans = int(in_chans * expand_ratio)
|
||||
self.out_chans = out_chans
|
||||
|
||||
self.conv1 = Conv2d_BN(in_chans, self.hidden_chans, ks=1)
|
||||
self.act1 = activation()
|
||||
|
||||
self.conv2 = Conv2d_BN(self.hidden_chans, self.hidden_chans, ks=3, stride=1, pad=1, groups=self.hidden_chans)
|
||||
self.act2 = activation()
|
||||
|
||||
self.conv3 = Conv2d_BN(self.hidden_chans, out_chans, ks=1, bn_weight_init=0.0)
|
||||
self.act3 = activation()
|
||||
|
||||
# NOTE: `DropPath` is needed only for training.
|
||||
# self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||
self.drop_path = nn.Identity()
|
||||
|
||||
def forward(self, x):
|
||||
shortcut = x
|
||||
|
||||
x = self.conv1(x)
|
||||
x = self.act1(x)
|
||||
|
||||
x = self.conv2(x)
|
||||
x = self.act2(x)
|
||||
|
||||
x = self.conv3(x)
|
||||
|
||||
x = self.drop_path(x)
|
||||
|
||||
x += shortcut
|
||||
x = self.act3(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class PatchMerging(nn.Module):
|
||||
|
||||
def __init__(self, input_resolution, dim, out_dim, activation):
|
||||
super().__init__()
|
||||
|
||||
self.input_resolution = input_resolution
|
||||
self.dim = dim
|
||||
self.out_dim = out_dim
|
||||
self.act = activation()
|
||||
self.conv1 = Conv2d_BN(dim, out_dim, 1, 1, 0)
|
||||
stride_c = 2
|
||||
if (out_dim == 320 or out_dim == 448 or out_dim == 576):
|
||||
stride_c = 1
|
||||
self.conv2 = Conv2d_BN(out_dim, out_dim, 3, stride_c, 1, groups=out_dim)
|
||||
self.conv3 = Conv2d_BN(out_dim, out_dim, 1, 1, 0)
|
||||
|
||||
def forward(self, x):
|
||||
if x.ndim == 3:
|
||||
H, W = self.input_resolution
|
||||
B = len(x)
|
||||
# (B, C, H, W)
|
||||
x = x.view(B, H, W, -1).permute(0, 3, 1, 2)
|
||||
|
||||
x = self.conv1(x)
|
||||
x = self.act(x)
|
||||
|
||||
x = self.conv2(x)
|
||||
x = self.act(x)
|
||||
x = self.conv3(x)
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
return x
|
||||
|
||||
|
||||
class ConvLayer(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim,
|
||||
input_resolution,
|
||||
depth,
|
||||
activation,
|
||||
drop_path=0.,
|
||||
downsample=None,
|
||||
use_checkpoint=False,
|
||||
out_dim=None,
|
||||
conv_expand_ratio=4.,
|
||||
):
|
||||
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.input_resolution = input_resolution
|
||||
self.depth = depth
|
||||
self.use_checkpoint = use_checkpoint
|
||||
|
||||
# build blocks
|
||||
self.blocks = nn.ModuleList([
|
||||
MBConv(
|
||||
dim,
|
||||
dim,
|
||||
conv_expand_ratio,
|
||||
activation,
|
||||
drop_path[i] if isinstance(drop_path, list) else drop_path,
|
||||
) for i in range(depth)])
|
||||
|
||||
# patch merging layer
|
||||
if downsample is not None:
|
||||
self.downsample = downsample(input_resolution, dim=dim, out_dim=out_dim, activation=activation)
|
||||
else:
|
||||
self.downsample = None
|
||||
|
||||
def forward(self, x):
|
||||
for blk in self.blocks:
|
||||
if self.use_checkpoint:
|
||||
x = checkpoint.checkpoint(blk, x)
|
||||
else:
|
||||
x = blk(x)
|
||||
if self.downsample is not None:
|
||||
x = self.downsample(x)
|
||||
return x
|
||||
|
||||
|
||||
class Mlp(nn.Module):
|
||||
|
||||
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
|
||||
super().__init__()
|
||||
out_features = out_features or in_features
|
||||
hidden_features = hidden_features or in_features
|
||||
self.norm = nn.LayerNorm(in_features)
|
||||
self.fc1 = nn.Linear(in_features, hidden_features)
|
||||
self.fc2 = nn.Linear(hidden_features, out_features)
|
||||
self.act = act_layer()
|
||||
self.drop = nn.Dropout(drop)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.norm(x)
|
||||
|
||||
x = self.fc1(x)
|
||||
x = self.act(x)
|
||||
x = self.drop(x)
|
||||
x = self.fc2(x)
|
||||
x = self.drop(x)
|
||||
return x
|
||||
|
||||
|
||||
class Attention(torch.nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim,
|
||||
key_dim,
|
||||
num_heads=8,
|
||||
attn_ratio=4,
|
||||
resolution=(14, 14),
|
||||
):
|
||||
super().__init__()
|
||||
# (h, w)
|
||||
assert isinstance(resolution, tuple) and len(resolution) == 2
|
||||
self.num_heads = num_heads
|
||||
self.scale = key_dim ** -0.5
|
||||
self.key_dim = key_dim
|
||||
self.nh_kd = nh_kd = key_dim * num_heads
|
||||
self.d = int(attn_ratio * key_dim)
|
||||
self.dh = int(attn_ratio * key_dim) * num_heads
|
||||
self.attn_ratio = attn_ratio
|
||||
h = self.dh + nh_kd * 2
|
||||
|
||||
self.norm = nn.LayerNorm(dim)
|
||||
self.qkv = nn.Linear(dim, h)
|
||||
self.proj = nn.Linear(self.dh, dim)
|
||||
|
||||
points = list(itertools.product(range(resolution[0]), range(resolution[1])))
|
||||
N = len(points)
|
||||
attention_offsets = {}
|
||||
idxs = []
|
||||
for p1 in points:
|
||||
for p2 in points:
|
||||
offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1]))
|
||||
if offset not in attention_offsets:
|
||||
attention_offsets[offset] = len(attention_offsets)
|
||||
idxs.append(attention_offsets[offset])
|
||||
self.attention_biases = torch.nn.Parameter(torch.zeros(num_heads, len(attention_offsets)))
|
||||
self.register_buffer('attention_bias_idxs', torch.LongTensor(idxs).view(N, N), persistent=False)
|
||||
|
||||
@torch.no_grad()
|
||||
def train(self, mode=True):
|
||||
super().train(mode)
|
||||
if mode and hasattr(self, 'ab'):
|
||||
del self.ab
|
||||
else:
|
||||
self.ab = self.attention_biases[:, self.attention_bias_idxs]
|
||||
|
||||
def forward(self, x): # x (B,N,C)
|
||||
B, N, _ = x.shape
|
||||
|
||||
# Normalization
|
||||
x = self.norm(x)
|
||||
|
||||
qkv = self.qkv(x)
|
||||
# (B, N, num_heads, d)
|
||||
q, k, v = qkv.view(B, N, self.num_heads, -1).split([self.key_dim, self.key_dim, self.d], dim=3)
|
||||
# (B, num_heads, N, d)
|
||||
q = q.permute(0, 2, 1, 3)
|
||||
k = k.permute(0, 2, 1, 3)
|
||||
v = v.permute(0, 2, 1, 3)
|
||||
self.ab = self.ab.to(self.attention_biases.device)
|
||||
|
||||
attn = ((q @ k.transpose(-2, -1)) * self.scale +
|
||||
(self.attention_biases[:, self.attention_bias_idxs] if self.training else self.ab))
|
||||
attn = attn.softmax(dim=-1)
|
||||
x = (attn @ v).transpose(1, 2).reshape(B, N, self.dh)
|
||||
x = self.proj(x)
|
||||
return x
|
||||
|
||||
|
||||
class TinyViTBlock(nn.Module):
|
||||
r""" TinyViT Block.
|
||||
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
input_resolution (tuple[int, int]): Input resolution.
|
||||
num_heads (int): Number of attention heads.
|
||||
window_size (int): Window size.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
|
||||
drop (float, optional): Dropout rate. Default: 0.0
|
||||
drop_path (float, optional): Stochastic depth rate. Default: 0.0
|
||||
local_conv_size (int): the kernel size of the convolution between
|
||||
Attention and MLP. Default: 3
|
||||
activation (torch.nn): the activation function. Default: nn.GELU
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim,
|
||||
input_resolution,
|
||||
num_heads,
|
||||
window_size=7,
|
||||
mlp_ratio=4.,
|
||||
drop=0.,
|
||||
drop_path=0.,
|
||||
local_conv_size=3,
|
||||
activation=nn.GELU,
|
||||
):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.input_resolution = input_resolution
|
||||
self.num_heads = num_heads
|
||||
assert window_size > 0, 'window_size must be greater than 0'
|
||||
self.window_size = window_size
|
||||
self.mlp_ratio = mlp_ratio
|
||||
|
||||
# NOTE: `DropPath` is needed only for training.
|
||||
# self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||
self.drop_path = nn.Identity()
|
||||
|
||||
assert dim % num_heads == 0, 'dim must be divisible by num_heads'
|
||||
head_dim = dim // num_heads
|
||||
|
||||
window_resolution = (window_size, window_size)
|
||||
self.attn = Attention(dim, head_dim, num_heads, attn_ratio=1, resolution=window_resolution)
|
||||
|
||||
mlp_hidden_dim = int(dim * mlp_ratio)
|
||||
mlp_activation = activation
|
||||
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=mlp_activation, drop=drop)
|
||||
|
||||
pad = local_conv_size // 2
|
||||
self.local_conv = Conv2d_BN(dim, dim, ks=local_conv_size, stride=1, pad=pad, groups=dim)
|
||||
|
||||
def forward(self, x):
|
||||
H, W = self.input_resolution
|
||||
B, L, C = x.shape
|
||||
assert L == H * W, 'input feature has wrong size'
|
||||
res_x = x
|
||||
if H == self.window_size and W == self.window_size:
|
||||
x = self.attn(x)
|
||||
else:
|
||||
x = x.view(B, H, W, C)
|
||||
pad_b = (self.window_size - H % self.window_size) % self.window_size
|
||||
pad_r = (self.window_size - W % self.window_size) % self.window_size
|
||||
padding = pad_b > 0 or pad_r > 0
|
||||
|
||||
if padding:
|
||||
x = F.pad(x, (0, 0, 0, pad_r, 0, pad_b))
|
||||
|
||||
pH, pW = H + pad_b, W + pad_r
|
||||
nH = pH // self.window_size
|
||||
nW = pW // self.window_size
|
||||
# window partition
|
||||
x = x.view(B, nH, self.window_size, nW, self.window_size,
|
||||
C).transpose(2, 3).reshape(B * nH * nW, self.window_size * self.window_size, C)
|
||||
x = self.attn(x)
|
||||
# window reverse
|
||||
x = x.view(B, nH, nW, self.window_size, self.window_size, C).transpose(2, 3).reshape(B, pH, pW, C)
|
||||
|
||||
if padding:
|
||||
x = x[:, :H, :W].contiguous()
|
||||
|
||||
x = x.view(B, L, C)
|
||||
|
||||
x = res_x + self.drop_path(x)
|
||||
|
||||
x = x.transpose(1, 2).reshape(B, C, H, W)
|
||||
x = self.local_conv(x)
|
||||
x = x.view(B, C, L).transpose(1, 2)
|
||||
|
||||
x = x + self.drop_path(self.mlp(x))
|
||||
return x
|
||||
|
||||
def extra_repr(self) -> str:
|
||||
return f'dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, ' \
|
||||
f'window_size={self.window_size}, mlp_ratio={self.mlp_ratio}'
|
||||
|
||||
|
||||
class BasicLayer(nn.Module):
|
||||
""" A basic TinyViT layer for one stage.
|
||||
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
input_resolution (tuple[int]): Input resolution.
|
||||
depth (int): Number of blocks.
|
||||
num_heads (int): Number of attention heads.
|
||||
window_size (int): Local window size.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
|
||||
drop (float, optional): Dropout rate. Default: 0.0
|
||||
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
|
||||
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
|
||||
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
|
||||
local_conv_size (int): the kernel size of the depthwise convolution between attention and MLP. Default: 3
|
||||
activation (torch.nn): the activation function. Default: nn.GELU
|
||||
out_dim (int | optional): the output dimension of the layer. Default: None
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim,
|
||||
input_resolution,
|
||||
depth,
|
||||
num_heads,
|
||||
window_size,
|
||||
mlp_ratio=4.,
|
||||
drop=0.,
|
||||
drop_path=0.,
|
||||
downsample=None,
|
||||
use_checkpoint=False,
|
||||
local_conv_size=3,
|
||||
activation=nn.GELU,
|
||||
out_dim=None,
|
||||
):
|
||||
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.input_resolution = input_resolution
|
||||
self.depth = depth
|
||||
self.use_checkpoint = use_checkpoint
|
||||
|
||||
# build blocks
|
||||
self.blocks = nn.ModuleList([
|
||||
TinyViTBlock(
|
||||
dim=dim,
|
||||
input_resolution=input_resolution,
|
||||
num_heads=num_heads,
|
||||
window_size=window_size,
|
||||
mlp_ratio=mlp_ratio,
|
||||
drop=drop,
|
||||
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
|
||||
local_conv_size=local_conv_size,
|
||||
activation=activation,
|
||||
) for i in range(depth)])
|
||||
|
||||
# patch merging layer
|
||||
if downsample is not None:
|
||||
self.downsample = downsample(input_resolution, dim=dim, out_dim=out_dim, activation=activation)
|
||||
else:
|
||||
self.downsample = None
|
||||
|
||||
def forward(self, x):
|
||||
for blk in self.blocks:
|
||||
if self.use_checkpoint:
|
||||
x = checkpoint.checkpoint(blk, x)
|
||||
else:
|
||||
x = blk(x)
|
||||
if self.downsample is not None:
|
||||
x = self.downsample(x)
|
||||
return x
|
||||
|
||||
def extra_repr(self) -> str:
|
||||
return f'dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}'
|
||||
|
||||
|
||||
class LayerNorm2d(nn.Module):
|
||||
|
||||
def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
|
||||
super().__init__()
|
||||
self.weight = nn.Parameter(torch.ones(num_channels))
|
||||
self.bias = nn.Parameter(torch.zeros(num_channels))
|
||||
self.eps = eps
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
u = x.mean(1, keepdim=True)
|
||||
s = (x - u).pow(2).mean(1, keepdim=True)
|
||||
x = (x - u) / torch.sqrt(s + self.eps)
|
||||
x = self.weight[:, None, None] * x + self.bias[:, None, None]
|
||||
return x
|
||||
|
||||
|
||||
class TinyViT(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
img_size=224,
|
||||
in_chans=3,
|
||||
num_classes=1000,
|
||||
embed_dims=[96, 192, 384, 768],
|
||||
depths=[2, 2, 6, 2],
|
||||
num_heads=[3, 6, 12, 24],
|
||||
window_sizes=[7, 7, 14, 7],
|
||||
mlp_ratio=4.,
|
||||
drop_rate=0.,
|
||||
drop_path_rate=0.1,
|
||||
use_checkpoint=False,
|
||||
mbconv_expand_ratio=4.0,
|
||||
local_conv_size=3,
|
||||
layer_lr_decay=1.0,
|
||||
):
|
||||
super().__init__()
|
||||
self.img_size = img_size
|
||||
self.num_classes = num_classes
|
||||
self.depths = depths
|
||||
self.num_layers = len(depths)
|
||||
self.mlp_ratio = mlp_ratio
|
||||
|
||||
activation = nn.GELU
|
||||
|
||||
self.patch_embed = PatchEmbed(in_chans=in_chans,
|
||||
embed_dim=embed_dims[0],
|
||||
resolution=img_size,
|
||||
activation=activation)
|
||||
|
||||
patches_resolution = self.patch_embed.patches_resolution
|
||||
self.patches_resolution = patches_resolution
|
||||
|
||||
# stochastic depth
|
||||
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
|
||||
|
||||
# build layers
|
||||
self.layers = nn.ModuleList()
|
||||
for i_layer in range(self.num_layers):
|
||||
kwargs = dict(
|
||||
dim=embed_dims[i_layer],
|
||||
input_resolution=(patches_resolution[0] // (2 ** (i_layer - 1 if i_layer == 3 else i_layer)),
|
||||
patches_resolution[1] // (2 ** (i_layer - 1 if i_layer == 3 else i_layer))),
|
||||
# input_resolution=(patches_resolution[0] // (2 ** i_layer),
|
||||
# patches_resolution[1] // (2 ** i_layer)),
|
||||
depth=depths[i_layer],
|
||||
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
|
||||
downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
|
||||
use_checkpoint=use_checkpoint,
|
||||
out_dim=embed_dims[min(i_layer + 1,
|
||||
len(embed_dims) - 1)],
|
||||
activation=activation,
|
||||
)
|
||||
if i_layer == 0:
|
||||
layer = ConvLayer(
|
||||
conv_expand_ratio=mbconv_expand_ratio,
|
||||
**kwargs,
|
||||
)
|
||||
else:
|
||||
layer = BasicLayer(num_heads=num_heads[i_layer],
|
||||
window_size=window_sizes[i_layer],
|
||||
mlp_ratio=self.mlp_ratio,
|
||||
drop=drop_rate,
|
||||
local_conv_size=local_conv_size,
|
||||
**kwargs)
|
||||
self.layers.append(layer)
|
||||
|
||||
# Classifier head
|
||||
self.norm_head = nn.LayerNorm(embed_dims[-1])
|
||||
self.head = nn.Linear(embed_dims[-1], num_classes) if num_classes > 0 else torch.nn.Identity()
|
||||
|
||||
# init weights
|
||||
self.apply(self._init_weights)
|
||||
self.set_layer_lr_decay(layer_lr_decay)
|
||||
self.neck = nn.Sequential(
|
||||
nn.Conv2d(
|
||||
embed_dims[-1],
|
||||
256,
|
||||
kernel_size=1,
|
||||
bias=False,
|
||||
),
|
||||
LayerNorm2d(256),
|
||||
nn.Conv2d(
|
||||
256,
|
||||
256,
|
||||
kernel_size=3,
|
||||
padding=1,
|
||||
bias=False,
|
||||
),
|
||||
LayerNorm2d(256),
|
||||
)
|
||||
|
||||
def set_layer_lr_decay(self, layer_lr_decay):
|
||||
decay_rate = layer_lr_decay
|
||||
|
||||
# layers -> blocks (depth)
|
||||
depth = sum(self.depths)
|
||||
lr_scales = [decay_rate ** (depth - i - 1) for i in range(depth)]
|
||||
|
||||
def _set_lr_scale(m, scale):
|
||||
for p in m.parameters():
|
||||
p.lr_scale = scale
|
||||
|
||||
self.patch_embed.apply(lambda x: _set_lr_scale(x, lr_scales[0]))
|
||||
i = 0
|
||||
for layer in self.layers:
|
||||
for block in layer.blocks:
|
||||
block.apply(lambda x: _set_lr_scale(x, lr_scales[i]))
|
||||
i += 1
|
||||
if layer.downsample is not None:
|
||||
layer.downsample.apply(lambda x: _set_lr_scale(x, lr_scales[i - 1]))
|
||||
assert i == depth
|
||||
for m in [self.norm_head, self.head]:
|
||||
m.apply(lambda x: _set_lr_scale(x, lr_scales[-1]))
|
||||
|
||||
for k, p in self.named_parameters():
|
||||
p.param_name = k
|
||||
|
||||
def _check_lr_scale(m):
|
||||
for p in m.parameters():
|
||||
assert hasattr(p, 'lr_scale'), p.param_name
|
||||
|
||||
self.apply(_check_lr_scale)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
# NOTE: This initialization is needed only for training.
|
||||
# trunc_normal_(m.weight, std=.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
|
||||
@torch.jit.ignore
|
||||
def no_weight_decay_keywords(self):
|
||||
return {'attention_biases'}
|
||||
|
||||
def forward_features(self, x):
|
||||
# x: (N, C, H, W)
|
||||
x = self.patch_embed(x)
|
||||
|
||||
x = self.layers[0](x)
|
||||
start_i = 1
|
||||
|
||||
for i in range(start_i, len(self.layers)):
|
||||
layer = self.layers[i]
|
||||
x = layer(x)
|
||||
B, _, C = x.size()
|
||||
x = x.view(B, 64, 64, C)
|
||||
x = x.permute(0, 3, 1, 2)
|
||||
x = self.neck(x)
|
||||
return x
|
||||
|
||||
def forward(self, x):
|
||||
x = self.forward_features(x)
|
||||
return x
|
||||
Loading…
Reference in new issue