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refine tests and examples

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Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/8898823
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yingda.chen
2022-06-01 10:20:53 +08:00
parent 1d01a78c2b
commit f8eb699f7f
3 changed files with 53 additions and 47 deletions
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docs/source/quick_start.md
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@@ -20,7 +20,7 @@ which pip
## 第三方依赖安装
MaaS Library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:
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:
* [Pytorch安装指导](https://pytorch.org/get-started/locally/)
* [Tensorflow安装指导](https://www.tensorflow.org/install/pip)
@@ -41,7 +41,7 @@ python -c "from maas_lib.pipelines import pipeline;print(pipeline('image-matting
```
### 使用源码
### 使用源码安装
适合本地开发调试使用，修改源码后可以直接执行
```shell
@@ -64,7 +64,6 @@ python -c "from maas_lib.pipelines import pipeline;print(pipeline('image-matting
```
## 训练
to be done
@@ -84,12 +83,33 @@ from maas_lib.pipelines import pipeline
from maas_lib.utils.constant import Tasks
# 根据任务名创建pipeline
img_matting = pipeline(
Tasks.image_matting, model='damo/image-matting-person')
img_matting = pipeline(Tasks.image_matting, model='damo/image-matting-person')
# 直接提供图像文件的url作为pipeline推理的输入
result = img_matting(
'http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/data/test/maas/image_matting/test.png'
)
cv2.imwrite('result.png', result['output_png'])
print(f'result file path is {osp.abspath("result.png")}')
print(f'Output written to {osp.abspath("result.png")}')
```
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
```python
import cv2
import os.path as osp
from maas_lib.pipelines import pipeline
from maas_lib.utils.constant import Tasks
from ali_maas_datasets import PyDataset
# 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
input_location = [
'http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/data/test/maas/image_matting/test.png'
]
dataset = PyDataset.load(input_location, target='image')
img_matting = pipeline(Tasks.image_matting, model='damo/image-matting-person')
# 输入为PyDataset时，输出的结果为迭代器
result = img_matting(dataset)
cv2.imwrite('result.png', next(result)['output_png'])
print(f'Output written to {osp.abspath("result.png")}')
```