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Using PaddleHub through Python Code Execution

The codes/commands on this page can run online on AIStudio. It is similar to the notebook environment, which can be accessed through a browser, without environment preparation. This is a quick and easy experiences for developers.

PaddleHub example for computer vision tasks

Taking the computer vision task as an example, we choose a test image test.jpg to implement the following four functions:

Note: If you need to find out which pre-training models can be executed from PaddleHub, get the model name (for example, deeplabv3p_xception65_humanseg, by which the model will be executed in subsequent codes). For details, see official website document. In the documents, it is easy to search because models are classified according to model categories, with providing a detailed description of the models.

Before experience, install the PaddleHub.

# install the latest version
$ pip install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple

Original Image Display

# Download a picture
$ wget https://paddlehub.bj.bcebos.com/resources/test_image.jpg
--2020-07-22 12:22:19--  https://paddlehub.bj.bcebos.com/resources/test_image.jpg
Resolving paddlehub.bj.bcebos.com (paddlehub.bj.bcebos.com)... 182.61.200.195, 182.61.200.229
Connecting to paddlehub.bj.bcebos.com (paddlehub.bj.bcebos.com)|182.61.200.195|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 967120 (944K) [image/jpeg]
Saving to: ‘test_image.jpg.1’

test_image.jpg.1    100%[===================>] 944.45K  5.51MB/s    in 0.2s  

2020-07-22 12:22:19 (5.51 MB/s) - ‘test_image.jpg.1’ saved [967120/967120]

png

Portrait Cutout

PaddleHub adopts the model-based software design concept. All pre-training models are similar to Python packages, with the concept of version. You can install, upgrade, and remove the model conveniently by running the commands hub install and hub uninstall:

By default, you can download the model of the latest version by running the following command. If you want to specify the version, you can add the version number after the ==1.1.1 command.

#intall pre-trained models
$ hub install deeplabv3p_xception65_humanseg
Downloading deeplabv3p_xception65_humanseg
[==================================================] 100.00%
Uncompress /home/aistudio/.paddlehub/tmp/tmpo32jeve0/deeplabv3p_xception65_humanseg
[==================================================] 100.00%
Successfully installed deeplabv3p_xception65_humanseg-1.1.1

# import paddlehub
import paddlehub as hub
module = hub.Module(name="deeplabv3p_xception65_humanseg")
res = module.segmentation(paths = ["./test_image.jpg"], visualization=True, output_dir='humanseg_output')
[32m[2020-07-22 12:22:49,474] [    INFO] - Installing deeplabv3p_xception65_humanseg module [0m

​
Downloading deeplabv3p_xception65_humanseg [==================================================] 100.00% Uncompress /home/aistudio/.paddlehub/tmp/tmpzrrl1duq/deeplabv3p_xception65_humanseg [==================================================] 100.00%

​
[32m[2020-07-22 12:23:11,811] [ INFO] - Successfully installed deeplabv3p_xception65_humanseg-1.1.1 [0m

png

As you can see, the execution of PaddleHub with Python codes requires only three lines of codes:

import paddlehub as hub  
module = hub.Module(name="deeplabv3p_xception65_humanseg")  
res = module.segmentation(paths = ["./test.jpg"], visualization=True, output_dir='humanseg_output')
  • Model names are specified through the hub.Module API.
  • module.segmentation is used to execute the image segmentation prediction tasks. Different prediction APIs are designed for different types of tasks. For example, the face detection task uses the face_detection function. It is recommended to view the corresponding model introduction document before the pre-training model is executed.
  • The prediction results are saved in output_dir='humanseg_output' directory, and you can view the output images in this directory.

For the implementation of other tasks, refer to this pattern. Let's see how the next few tasks are implemented.

Body Part Segmentation

$ hub install ace2p
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/sklearn/externals/joblib/externals/cloudpickle/cloudpickle.py:47: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses
  import imp
Downloading ace2p
[==================================================] 100.00%
Uncompress /home/aistudio/.paddlehub/tmp/tmpfsovt3f8/ace2p
[==================================================] 100.00%
Successfully installed ace2p-1.1.0

import paddlehub as hub
module = hub.Module(name="ace2p")
res = module.segmentation(paths = ["./test_image.jpg"], visualization=True, output_dir='ace2p_output')
[32m[2020-07-22 12:23:58,027] [    INFO] - Installing ace2p module [0m

​
Downloading ace2p [==================================================] 100.00% Uncompress /home/aistudio/.paddlehub/tmp/tmptrogpj6j/ace2p [==================================================] 100.00%

​
[32m[2020-07-22 12:24:22,575] [ INFO] - Successfully installed ace2p-1.1.0 [0m

png

Face Detection

#install pre-trained model
$ hub install ultra_light_fast_generic_face_detector_1mb_640
Downloading ultra_light_fast_generic_face_detector_1mb_640
[==================================================] 100.00%
Uncompress /home/aistudio/.paddlehub/tmp/tmpz82xnmy6/ultra_light_fast_generic_face_detector_1mb_640
[==================================================] 100.00%
Successfully installed ultra_light_fast_generic_face_detector_1mb_640-1.1.2

import paddlehub as hub
module = hub.Module(name="ultra_light_fast_generic_face_detector_1mb_640")
res = module.face_detection(paths = ["./test_image.jpg"], visualization=True, output_dir='face_detection_output')
[32m[2020-07-22 12:25:12,948] [    INFO] - Installing ultra_light_fast_generic_face_detector_1mb_640 module [0m

​
Downloading ultra_light_fast_generic_face_detector_1mb_640 [==================================================] 100.00% Uncompress /home/aistudio/.paddlehub/tmp/tmpw44mo56p/ultra_light_fast_generic_face_detector_1mb_640 [==================================================] 100.00%

​
[32m[2020-07-22 12:25:14,698] [ INFO] - Successfully installed ultra_light_fast_generic_face_detector_1mb_640-1.1.2�[0m

png

Key Point Detection

$ hub install human_pose_estimation_resnet50_mpii
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/sklearn/externals/joblib/externals/cloudpickle/cloudpickle.py:47: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses
  import imp
Downloading human_pose_estimation_resnet50_mpii
[==================================================] 100.00%
Uncompress /home/aistudio/.paddlehub/tmp/tmpn_ppwkzq/human_pose_estimation_resnet50_mpii
[========                                          ] 17.99%

import paddlehub as hub
module = hub.Module(name="human_pose_estimation_resnet50_mpii")
res = module.keypoint_detection(paths = ["./test_image.jpg"], visualization=True, output_dir='keypoint_output')
[32m[2020-07-23 11:27:33,989] [    INFO] - Installing human_pose_estimation_resnet50_mpii module [0m
[32m[2020-07-23 11:27:33,992] [    INFO] - Module human_pose_estimation_resnet50_mpii already installed in /home/aistudio/.paddlehub/modules/human_pose_estimation_resnet50_mpii [0m

​
image saved in keypoint_output/test_imagetime=1595474855.jpg

png

PaddleHub Example for Natural Language Processing Tasks

Let's look at two more examples of natural language processing tasks: Chinese word segmentation and sentiment classification tasks.

Chinese word segmentation

$ hub install lac
2020-07-22 10:03:09,866-INFO: font search path ['/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/ttf', '/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/afm', '/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/pdfcorefonts']
2020-07-22 10:03:10,208-INFO: generated new fontManager
Downloading lac
[==================================================] 100.00%
Uncompress /home/aistudio/.paddlehub/tmp/tmp8ukaz690/lac
[==================================================] 100.00%
Successfully installed lac-2.1.1

import paddlehub as hub
lac = hub.Module(name="lac")
test_text = ["1996年，曾经是微软员工的加布·纽维尔和麦克·哈灵顿一同创建了Valve软件公司。他们在1996年下半年从id software取得了雷神之锤引擎的使用许可，用来开发半条命系列。"]
res = lac.lexical_analysis(texts = test_text)
print("The resuls are: ", res)
[32m[2020-07-22 10:03:18,439] [    INFO] - Installing lac module[0m
[32m[2020-07-22 10:03:18,531] [    INFO] - Module lac already installed in /home/aistudio/.paddlehub/modules/lac [0m

​
The resuls are: [{'word': ['1996年', '，', '曾经', '是', '微软', '员工', '的', '加布·纽维尔', '和', '麦克·哈灵顿', '一同', '创建', '了', 'Valve软件公司', '。', '他们', '在', '1996年下半年', '从', 'id', ' ', 'software', '取得', '了', '雷神之锤', '引擎', '的', '使用', '许可', '，', '用来', '开发', '半条命', '系列', '。'], 'tag': ['TIME', 'w', 'd', 'v', 'ORG', 'n', 'u', 'PER', 'c', 'PER', 'd', 'v', 'u', 'ORG', 'w', 'r', 'p', 'TIME', 'p', 'nz', 'w', 'n', 'v', 'u', 'n', 'n', 'u', 'vn', 'vn', 'w', 'v', 'v', 'n', 'n', 'w']}]

As you can see, compared to computer vision tasks, there are differences between the input and output interfaces (where you need to enter text, executed as the function parameters), because it depends on the task types. For details, see the API description for the corresponding pre-training model.

Sentiment classification

$ hub install senta_bilstm
Module senta_bilstm-1.1.0 already installed in /home/aistudio/.paddlehub/modules/senta_bilstm

import paddlehub as hub
senta = hub.Module(name="senta_bilstm")
test_text = ["味道不错，确实不算太辣，适合不能吃辣的人。就在长江边上，抬头就能看到长江的风景。鸭肠、黄鳝都比较新鲜。"]
res = senta.sentiment_classify(texts = test_text)

print("情感分析结果：", res)
[32m[2020-07-22 10:34:06,922] [    INFO] - Installing senta_bilstm module [0m
[32m[2020-07-22 10:34:06,984] [    INFO] - Module senta_bilstm already installed in /home/aistudio/.paddlehub/modules/senta_bilstm�[0m
[32m[2020-07-22 10:34:08,937] [    INFO] - Installing lac module[0m
[32m[2020-07-22 10:34:08,939] [    INFO] - Module lac already installed in /home/aistudio/.paddlehub/modules/lac [0m

​
情感分析结果： [{'text': '味道不错，确实不算太辣，适合不能吃辣的人。就在长江边上，抬头就能看到长江的风景。鸭肠、黄鳝都比较新鲜。', 'sentiment_label': 1, 'sentiment_key': 'positive', 'positive_probs': 0.9771, 'negative_probs': 0.0229}]

Summary

PaddleHub provides a rich set of pre-training models, including image classification, semantic model, video classification, image generation, image segmentation, text review, key point detection and other mainstream models. These can be executed quickly and easily with only 3 lines of Python codes, with the instant output of prediction results. You can try it out by selecting some models from the Pre-training Model List.