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116 lines
3.6 KiB
Markdown
116 lines
3.6 KiB
Markdown
# 快速开始
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## python环境配置
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首先，参考[文档](https://docs.anaconda.com/anaconda/install/) 安装配置Anaconda环境
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安装完成后，执行如下命令为maas library创建对应的python环境。
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```shell
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conda create -n maas python=3.6
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conda activate maas
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```
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检查python和pip命令是否切换到conda环境下。
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```shell
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which python
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# ~/workspace/anaconda3/envs/maas/bin/python
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which pip
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# ~/workspace/anaconda3/envs/maas/bin/pip
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```
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注： 本项目只支持`python3`环境，请勿使用python2环境。
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## 第三方依赖安装
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MaaS Library目前支持tensorflow，pytorch两大深度学习框架进行模型训练、推理， 在Python 3.6+, Pytorch 1.8+, Tensorflow 2.6上测试可运行，用户可以根据所选模型对应的计算框架进行安装，可以参考如下链接进行安装所需框架:
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* [Pytorch安装指导](https://pytorch.org/get-started/locally/)
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* [Tensorflow安装指导](https://www.tensorflow.org/install/pip)
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## MaaS library 安装
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注： 如果在安装过程中遇到错误，请前往[常见问题](faq.md)查找解决方案。
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### pip安装
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```shell
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pip install -r http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/release/maas/maas.txt
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```
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安装成功后，可以执行如下命令进行验证安装是否正确
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```shell
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python -c "from maas_lib.pipelines import pipeline;print(pipeline('image-matting',model='damo/image-matting-person')('http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/data/test/maas/image_matting/test.png'))"
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```
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### 使用源码安装
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适合本地开发调试使用，修改源码后可以直接执行
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```shell
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git clone git@gitlab.alibaba-inc.com:Ali-MaaS/MaaS-lib.git maaslib
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git fetch origin master
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git checkout master
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cd maaslib
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#安装依赖
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pip install -r requirements.txt
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# 设置PYTHONPATH
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export PYTHONPATH=`pwd`
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```
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安装成功后，可以执行如下命令进行验证安装是否正确
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```shell
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python -c "from maas_lib.pipelines import pipeline;print(pipeline('image-matting',model='damo/image-matting-person')('http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/data/test/maas/image_matting/test.png'))"
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```
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## 训练
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to be done
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## 评估
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to be done
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## 推理
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pipeline函数提供了简洁的推理接口，示例如下， 更多pipeline介绍和示例请参考[pipeline使用教程](tutorials/pipeline.md)
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```python
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import cv2
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import os.path as osp
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from maas_lib.pipelines import pipeline
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from maas_lib.utils.constant import Tasks
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# 根据任务名创建pipeline
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img_matting = pipeline(Tasks.image_matting, model='damo/image-matting-person')
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# 直接提供图像文件的url作为pipeline推理的输入
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result = img_matting(
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'http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/data/test/maas/image_matting/test.png'
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)
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cv2.imwrite('result.png', result['output_png'])
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print(f'Output written to {osp.abspath("result.png")}')
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```
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此外，pipeline接口也能接收Dataset作为输入，上面的代码同样可以实现为
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```python
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import cv2
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import os.path as osp
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from maas_lib.pipelines import pipeline
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from maas_lib.utils.constant import Tasks
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from ali_maas_datasets import PyDataset
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# 使用图像url构建PyDataset，此处也可通过 input_location = '/dir/to/images' 来使用本地文件夹
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input_location = [
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'http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/data/test/maas/image_matting/test.png'
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]
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dataset = PyDataset.load(input_location, target='image')
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img_matting = pipeline(Tasks.image_matting, model='damo/image-matting-person')
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# 输入为PyDataset时，输出的结果为迭代器
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result = img_matting(dataset)
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cv2.imwrite('result.png', next(result)['output_png'])
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print(f'Output written to {osp.abspath("result.png")}')
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```
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