【DSW Gallery】Grounded-SAM 马赛克熊使用示例

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简介: 由IDEA-CVR主导的Grounded-SAM(https://github.com/IDEA-Research/Grounded-Segment-Anything)项目,可以通过任意组合Foundation Models,实现各种视觉工作流场景的应用。 PAI-DSW的Grounded-SAM v0.1版本中最酷炫的功能是:可以实现只输入图片,就可以无交互式完全自动化标注出图片的检测框和分割掩码。

直接使用

请打开基于Grounded-SAM 马赛克熊使用示例,并点击右上角 “ 在DSW中打开” 。

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由IDEA-CVR主导的Grounded-SAM(https://github.com/IDEA-Research/Grounded-Segment-Anything)项目,可以通过任意组合Foundation Models,实现各种视觉工作流场景的应用。PAI-DSW的Grounded-SAM v0.1版本中最酷炫的功能是:可以实现只输入图片,就可以无交互式完全自动化标注出图片的检测框和分割掩码。

下面介绍一下Grounded-SAM提供的各种task_type模式:

det: 通过Grounding DINO(https://github.com/IDEA-Research/GroundingDINO)和文本交互实现检测(需要指定detection prompt)。
seg: 通过组合Grounding DINO和Segment Anything(https://github.com/facebookresearch/segment-anything)实现文本交互实现检测+分割(需要指定detection prompt)。
inpainting: 通过组合Grounding DINO + Segment Anything + Stable Diffusion(https://github.com/Stability-AI/StableDiffusion)实现文本交换替换目标物体(需要指定detection prompt和inpaint prompt)。
automatic: 通过组合BLIP(https://github.com/salesforce/BLIP) + Grounding DINO + Segment Anything实现无交互式检测+分割(无需指定prompt)。

Tips:

如果您想用Grounding DINO在一句话中检测多个对象, 我们建议用.分隔每个名称, 比如: cat.dog.chair。

安装依赖包

!git clone https://github.com/IDEA-Research/Grounded-Segment-Anything
!pip install gradio==3.16.2
%cd ./Grounded-Segment-Anything 
!pip install -r requirements.txt
%cd ./GroundingDINO
!pip install .
%cd ../segment_anything
!pip install .
%cd ../
fatal: destination path 'Grounded-Segment-Anything' already exists and is not an empty directory.
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WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
/mnt/workspace/workgroup/yunji.cjy/codebase/pai-dsw-examples/deepLearning/cv/grounded_sam/Grounded-Segment-Anything/GroundingDINO
Looking in indexes: http://yum.tbsite.net/pypi/simple
Processing /mnt/workspace/workgroup/yunji.cjy/codebase/pai-dsw-examples/deepLearning/cv/grounded_sam/Grounded-Segment-Anything/GroundingDINO
  Preparing metadata (setup.py) ... ?25ldone
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Building wheels for collected packages: groundingdino
  Building wheel for groundingdino (setup.py) ... ?25ldone
?25h  Created wheel for groundingdino: filename=groundingdino-0.1.0-cp39-cp39-linux_x86_64.whl size=320179 sha256=0608b8d71abd095353815bf6a1b2a791147f37aafca0c74cd5bae8a2525f4b16
  Stored in directory: /tmp/pip-ephem-wheel-cache-n1shgckw/wheels/14/e9/cf/99a7846bdadc6a78d1e650a7083f847dacdc7e55009eb0d77e
Successfully built groundingdino
Installing collected packages: groundingdino
  Attempting uninstall: groundingdino
    Found existing installation: groundingdino 0.1.0
    Uninstalling groundingdino-0.1.0:
      Successfully uninstalled groundingdino-0.1.0
Successfully installed groundingdino-0.1.0
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
/mnt/workspace/workgroup/yunji.cjy/codebase/pai-dsw-examples/deepLearning/cv/grounded_sam/Grounded-Segment-Anything/segment_anything
Looking in indexes: http://yum.tbsite.net/pypi/simple
Processing /mnt/workspace/workgroup/yunji.cjy/codebase/pai-dsw-examples/deepLearning/cv/grounded_sam/Grounded-Segment-Anything/segment_anything
  Preparing metadata (setup.py) ... ?25ldone
?25hBuilding wheels for collected packages: segment-anything
  Building wheel for segment-anything (setup.py) ... ?25ldone
?25h  Created wheel for segment-anything: filename=segment_anything-1.0-py3-none-any.whl size=36561 sha256=eef97d8cb3d96388426f9600b1d8d553a020210ec7e45443fe0984ee57b1c5d2
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Successfully built segment-anything
Installing collected packages: segment-anything
  Attempting uninstall: segment-anything
    Found existing installation: segment-anything 1.0
    Uninstalling segment-anything-1.0:
      Successfully uninstalled segment-anything-1.0
Successfully installed segment-anything-1.0
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
/mnt/workspace/workgroup/yunji.cjy/codebase/pai-dsw-examples/deepLearning/cv/grounded_sam/Grounded-Segment-Anything
!wget -c https://pai-vision-data-inner.oss-cn-zhangjiakou.aliyuncs.com/data/crawl_data/groundingdino_swint_ogc.pth
!wget -c https://pai-vision-data-inner.oss-cn-zhangjiakou.aliyuncs.com/data/crawl_data/sam_vit_h_4b8939.pth
--2023-04-13 15:35:41--  https://pai-vision-data-inner.oss-cn-zhangjiakou.aliyuncs.com/data/crawl_data/groundingdino_swint_ogc.pth
Resolving pai-vision-data-inner.oss-cn-zhangjiakou.aliyuncs.com (pai-vision-data-inner.oss-cn-zhangjiakou.aliyuncs.com)... 39.98.20.34
Connecting to pai-vision-data-inner.oss-cn-zhangjiakou.aliyuncs.com (pai-vision-data-inner.oss-cn-zhangjiakou.aliyuncs.com)|39.98.20.34|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 693997677 (662M) [application/octet-stream]
Saving to: ‘groundingdino_swint_ogc.pth’
groundingdino_swint 100%[===================>] 661.85M  23.2MB/s    in 29s     
2023-04-13 15:36:10 (22.9 MB/s) - ‘groundingdino_swint_ogc.pth’ saved [693997677/693997677]
--2023-04-13 15:36:10--  https://pai-vision-data-inner.oss-cn-zhangjiakou.aliyuncs.com/data/crawl_data/sam_vit_h_4b8939.pth
Resolving pai-vision-data-inner.oss-cn-zhangjiakou.aliyuncs.com (pai-vision-data-inner.oss-cn-zhangjiakou.aliyuncs.com)... 39.98.20.34
Connecting to pai-vision-data-inner.oss-cn-zhangjiakou.aliyuncs.com (pai-vision-data-inner.oss-cn-zhangjiakou.aliyuncs.com)|39.98.20.34|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 2564550879 (2.4G) [application/octet-stream]
Saving to: ‘sam_vit_h_4b8939.pth’
sam_vit_h_4b8939.pt 100%[===================>]   2.39G  17.7MB/s    in 2m 31s  
2023-04-13 15:38:41 (16.2 MB/s) - ‘sam_vit_h_4b8939.pth’ saved [2564550879/2564550879]
!python gradio_app.py
Namespace(debug=False, share=False, port=7589, no_gradio_queue=False)
/home/pai/lib/python3.9/site-packages/gradio/components.py:148: UserWarning: Unknown style parameter: preview
  warnings.warn(f"Unknown style parameter: {key}")
/home/pai/lib/python3.9/site-packages/gradio/components.py:148: UserWarning: Unknown style parameter: object_fit
  warnings.warn(f"Unknown style parameter: {key}")
Running on local URL:  http://0.0.0.0:7589
To create a public link, set `share=True` in `launch()`.
/home/pai/lib/python3.9/site-packages/torch/functional.py:478: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2894.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
final text_encoder_type: bert-base-uncased
Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias']
- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
_IncompatibleKeys(missing_keys=[], unexpected_keys=['label_enc.weight'])
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/home/pai/lib/python3.9/site-packages/transformers/generation/utils.py:1288: UserWarning: Using `max_length`'s default (20) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.
  warnings.warn(
Caption: there are three bears sitting on the shore of a lake
/home/pai/lib/python3.9/site-packages/transformers/modeling_utils.py:830: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.
  warnings.warn(
/home/pai/lib/python3.9/site-packages/torch/utils/checkpoint.py:25: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn("None of the inputs have requires_grad=True. Gradients will be None")
Before NMS: 8 boxes
After NMS: 6 boxes
Revise caption with number: there are three bears sitting on the shore of a lake
/home/pai/lib/python3.9/site-packages/transformers/modeling_utils.py:830: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.
  warnings.warn(
/home/pai/lib/python3.9/site-packages/torch/utils/checkpoint.py:25: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn("None of the inputs have requires_grad=True. Gradients will be None")
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EasyNLP提供多种模型的训练及预测功能,旨在帮助自然语言开发者方便快捷地构建模型并应用于生产。本文以中文文本生成为例,为您介绍如何在PAI-DSW中使用EasyNLP。
【DSW Gallery】基于预训练模型的多场景文本生成(以新闻标题生成为例)
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机器学习/深度学习 并行计算 数据可视化
【DSW Gallery】EasyCV-基于关键点的视频分类示例
EasyCV是基于Pytorch,以自监督学习和Transformer技术为核心的 all-in-one 视觉算法建模工具,并包含图像分类,度量学习,目标检测,姿态识别等视觉任务的SOTA算法。本文以基于关键点的视频分类为例,为您介绍如何在PAI-DSW中使用EasyCV。
【DSW Gallery】EasyCV-基于关键点的视频分类示例