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Add OmDet-Turbo (#31843)

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* Add template with add-new-model-like

* Add rough OmDetTurboEncoder and OmDetTurboDecoder

* Add working OmDetTurbo convert to hf

* Change OmDetTurbo encoder to RT-DETR encoder

* Add swin timm backbone as default, add always partition fix for swin timm

* Add labels and tasks caching

* Fix make fix-copies

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* Fix quality, style, copies

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* Add integrated test and fix copy, style, quality

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* Add fix different prompts in batch (key_padding_mask)

* Add key_padding_mask to custom multi-head attention module

* Replace attention_mask by key_padding_mask

* Remove OmDetTurboModel and refactor

* Refactor processing of classes and abstract use of timm backbone

* Add testing, fix output attentions and hidden states, add cache for anchors generation

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* Add OmdetTurboModel

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* [run-slow] omdet_turbo

* [run-slow] omdet_turbo

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* [run-slow] omdet_turbo

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Co-authored-by: Ubuntu <ubuntu@ip-172-31-91-248.ec2.internal>
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Yoni Gozlan
2024-09-25 13:26:28 -04:00
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# OmDet-Turbo
## Overview
The OmDet-Turbo model was proposed in [Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion Head](https://arxiv.org/abs/2403.06892) by Tiancheng Zhao, Peng Liu, Xuan He, Lu Zhang, Kyusong Lee. OmDet-Turbo incorporates components from RT-DETR and introduces a swift multimodal fusion module to achieve real-time open-vocabulary object detection capabilities while maintaining high accuracy. The base model achieves performance of up to 100.2 FPS and 53.4 AP on COCO zero-shot.
The abstract from the paper is the following:
*End-to-end transformer-based detectors (DETRs) have shown exceptional performance in both closed-set and open-vocabulary object detection (OVD) tasks through the integration of language modalities. However, their demanding computational requirements have hindered their practical application in real-time object detection (OD) scenarios. In this paper, we scrutinize the limitations of two leading models in the OVDEval benchmark, OmDet and Grounding-DINO, and introduce OmDet-Turbo. This novel transformer-based real-time OVD model features an innovative Efficient Fusion Head (EFH) module designed to alleviate the bottlenecks observed in OmDet and Grounding-DINO. Notably, OmDet-Turbo-Base achieves a 100.2 frames per second (FPS) with TensorRT and language cache techniques applied. Notably, in zero-shot scenarios on COCO and LVIS datasets, OmDet-Turbo achieves performance levels nearly on par with current state-of-the-art supervised models. Furthermore, it establishes new state-of-the-art benchmarks on ODinW and OVDEval, boasting an AP of 30.1 and an NMS-AP of 26.86, respectively. The practicality of OmDet-Turbo in industrial applications is underscored by its exceptional performance on benchmark datasets and superior inference speed, positioning it as a compelling choice for real-time object detection tasks.*
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/omdet_turbo_architecture.jpeg" alt="drawing" width="600"/>
<small> OmDet-Turbo architecture overview. Taken from the <a href="https://arxiv.org/abs/2403.06892">original paper</a>. </small>
This model was contributed by [yonigozlan](https://huggingface.co/yonigozlan).
The original code can be found [here](https://github.com/om-ai-lab/OmDet).
## Usage tips
One unique property of OmDet-Turbo compared to other zero-shot object detection models, such as [Grounding DINO](grounding-dino), is the decoupled classes and prompt embedding structure that allows caching of text embeddings. This means that the model needs both classes and task as inputs, where classes is a list of objects we want to detect and task is the grounded text used to guide open-vocabulary detection. This approach limits the scope of the open-vocabulary detection and makes the decoding process faster.
[`OmDetTurboProcessor`] is used to prepare the classes, task and image triplet. The task input is optional, and when not provided, it will default to `"Detect [class1], [class2], [class3], ..."`. To process the results from the model, one can use `post_process_grounded_object_detection` from [`OmDetTurboProcessor`]. Notably, this function takes in the input classes, as unlike other zero-shot object detection models, the decoupling of classes and task embeddings means that no decoding of the predicted class embeddings is needed in the post-processing step, and the predicted classes can be matched to the inputted ones directly.
## Usage example
### Single image inference
Here's how to load the model and prepare the inputs to perform zero-shot object detection on a single image:
```python
import requests
from PIL import Image
from transformers import AutoProcessor, OmDetTurboForObjectDetection
processor = AutoProcessor.from_pretrained("omlab/omdet-turbo-tiny")
model = OmDetTurboForObjectDetection.from_pretrained("omlab/omdet-turbo-tiny")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
classes = ["cat", "remote"]
inputs = processor(image, text=classes, return_tensors="pt")
outputs = model(**inputs)
# convert outputs (bounding boxes and class logits)
results = processor.post_process_grounded_object_detection(
outputs,
classes=classes,
target_sizes=[image.size[::-1]],
score_threshold=0.3,
nms_threshold=0.3,
)[0]
for score, class_name, box in zip(
results["scores"], results["classes"], results["boxes"]
):
box = [round(i, 1) for i in box.tolist()]
print(
f"Detected {class_name} with confidence "
f"{round(score.item(), 2)} at location {box}"
)
```
### Multi image inference
OmDet-Turbo can perform batched multi-image inference, with support for different text prompts and classes in the same batch:
```python
>>> import torch
>>> import requests
>>> from io import BytesIO
>>> from PIL import Image
>>> from transformers import AutoProcessor, OmDetTurboForObjectDetection
>>> processor = AutoProcessor.from_pretrained("omlab/omdet-turbo-swin-tiny-hf")
>>> model = OmDetTurboForObjectDetection.from_pretrained("omlab/omdet-turbo-swin-tiny-hf")
>>> url1 = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image1 = Image.open(BytesIO(requests.get(url1).content)).convert("RGB")
>>> classes1 = ["cat", "remote"]
>>> task1 = "Detect {}.".format(", ".join(classes1))
>>> url2 = "http://images.cocodataset.org/train2017/000000257813.jpg"
>>> image2 = Image.open(BytesIO(requests.get(url2).content)).convert("RGB")
>>> classes2 = ["boat"]
>>> task2 = "Detect everything that looks like a boat."
>>> url3 = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
>>> image3 = Image.open(BytesIO(requests.get(url3).content)).convert("RGB")
>>> classes3 = ["statue", "trees"]
>>> task3 = "Focus on the foreground, detect statue and trees."
>>> inputs = processor(
... images=[image1, image2, image3],
... text=[classes1, classes2, classes3],
... task=[task1, task2, task3],
... return_tensors="pt",
... )
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> # convert outputs (bounding boxes and class logits)
>>> results = processor.post_process_grounded_object_detection(
... outputs,
... classes=[classes1, classes2, classes3],
... target_sizes=[image1.size[::-1], image2.size[::-1], image3.size[::-1]],
... score_threshold=0.2,
... nms_threshold=0.3,
... )
>>> for i, result in enumerate(results):
... for score, class_name, box in zip(
... result["scores"], result["classes"], result["boxes"]
... ):
... box = [round(i, 1) for i in box.tolist()]
... print(
... f"Detected {class_name} with confidence "
... f"{round(score.item(), 2)} at location {box} in image {i}"
... )
Detected remote with confidence 0.77 at location [39.9, 70.4, 176.7, 118.0] in image 0
Detected cat with confidence 0.72 at location [11.6, 54.2, 314.8, 474.0] in image 0
Detected remote with confidence 0.56 at location [333.4, 75.8, 370.7, 187.0] in image 0
Detected cat with confidence 0.55 at location [345.2, 24.0, 639.8, 371.7] in image 0
Detected boat with confidence 0.32 at location [146.9, 219.8, 209.6, 250.7] in image 1
Detected boat with confidence 0.3 at location [319.1, 223.2, 403.2, 238.4] in image 1
Detected boat with confidence 0.27 at location [37.7, 220.3, 84.0, 235.9] in image 1
Detected boat with confidence 0.22 at location [407.9, 207.0, 441.7, 220.2] in image 1
Detected statue with confidence 0.73 at location [544.7, 210.2, 651.9, 502.8] in image 2
Detected trees with confidence 0.25 at location [3.9, 584.3, 391.4, 785.6] in image 2
Detected trees with confidence 0.25 at location [1.4, 621.2, 118.2, 787.8] in image 2
Detected statue with confidence 0.2 at location [428.1, 205.5, 767.3, 759.5] in image 2
```
## OmDetTurboConfig
[[autodoc]] OmDetTurboConfig
## OmDetTurboProcessor
[[autodoc]] OmDetTurboProcessor
- post_process_grounded_object_detection
## OmDetTurboForObjectDetection
[[autodoc]] OmDetTurboForObjectDetection
- forward