From 0b14d33654b081de48a8eb9be4f172c614ad9cac Mon Sep 17 00:00:00 2001 From: ShenYuhan Date: Mon, 8 Jun 2020 17:29:44 +0800 Subject: [PATCH] Modify serving docs (#674) * Modify docs for hub-serving --- demo/serving/README.md | 32 +---- .../module_serving/GAN_stgan_celeba/README.md | 84 ----------- .../stgan_celeba_serving_demo.py | 26 ---- demo/serving/module_serving/README.md | 28 +--- .../classification_vgg11_imagenet/README.md | 76 ---------- .../vgg11_imagenet_serving_demo.py | 18 --- .../lac_2.1.0_serving_demo.py | 19 --- .../lexical_analysis_lac/lac_serving_demo.py | 15 +- .../lac_with_dict_serving_demo.py | 16 --- .../README.md | 41 ++---- ...dbox_lite_server_mask_file_serving_demo.py | 28 ---- ...yramidbox_lite_server_mask_serving_demo.py | 33 +++-- .../README.md | 135 ------------------ .../output/cat.jpg | Bin 128218 -> 0 bytes .../output/dog.jpg | Bin 60314 -> 0 bytes .../yolov3_darknet53_coco2017_serving_demo.py | 26 ---- .../semantic_model_simnet_bow/README.md | 80 ----------- .../simnet_bow_serving_demo.py | 17 --- .../README.md | 91 ------------ ...labv3p_xception65_humanseg_serving_demo.py | 26 ---- .../sentiment_analysis_senta_lstm/README.md | 78 ---------- .../senta_lstm_serving_demo.py | 14 -- .../README.md | 79 ---------- .../porn_detection_lstm_serving_demo.py | 14 -- docs/tutorial/serving.md | 89 +++--------- 25 files changed, 58 insertions(+), 1007 deletions(-) delete mode 100644 demo/serving/module_serving/GAN_stgan_celeba/README.md delete mode 100644 demo/serving/module_serving/GAN_stgan_celeba/stgan_celeba_serving_demo.py delete mode 100644 demo/serving/module_serving/classification_vgg11_imagenet/README.md delete mode 100644 demo/serving/module_serving/classification_vgg11_imagenet/vgg11_imagenet_serving_demo.py delete mode 100644 demo/serving/module_serving/lexical_analysis_lac/lac_2.1.0_serving_demo.py delete mode 100644 demo/serving/module_serving/lexical_analysis_lac/lac_with_dict_serving_demo.py delete mode 100644 demo/serving/module_serving/object_detection_pyramidbox_lite_server_mask/pyramidbox_lite_server_mask_file_serving_demo.py delete mode 100644 demo/serving/module_serving/object_detection_yolov3_darknet53_coco2017/README.md delete mode 100644 demo/serving/module_serving/object_detection_yolov3_darknet53_coco2017/output/cat.jpg delete mode 100644 demo/serving/module_serving/object_detection_yolov3_darknet53_coco2017/output/dog.jpg delete mode 100644 demo/serving/module_serving/object_detection_yolov3_darknet53_coco2017/yolov3_darknet53_coco2017_serving_demo.py delete mode 100644 demo/serving/module_serving/semantic_model_simnet_bow/README.md delete mode 100644 demo/serving/module_serving/semantic_model_simnet_bow/simnet_bow_serving_demo.py delete mode 100644 demo/serving/module_serving/semantic_segmentation_deeplabv3p_xception65_humanseg/README.md delete mode 100644 demo/serving/module_serving/semantic_segmentation_deeplabv3p_xception65_humanseg/deeplabv3p_xception65_humanseg_serving_demo.py delete mode 100644 demo/serving/module_serving/sentiment_analysis_senta_lstm/README.md delete mode 100644 demo/serving/module_serving/sentiment_analysis_senta_lstm/senta_lstm_serving_demo.py delete mode 100644 demo/serving/module_serving/text_censorship_porn_detection_lstm/README.md delete mode 100644 demo/serving/module_serving/text_censorship_porn_detection_lstm/porn_detection_lstm_serving_demo.py diff --git a/demo/serving/README.md b/demo/serving/README.md index 1095366e..5073ec0e 100644 --- a/demo/serving/README.md +++ b/demo/serving/README.md @@ -19,38 +19,12 @@ PaddleHub Serving主要包括利用Bert Service实现embedding服务化，以及 关于预训练模型一键服务部署的具体信息请参见[PaddleHub Serving](../../docs/tutorial/serving.md) -预训练模型一键服务部署包括以下示例： - -* [图像分类-基于vgg11_imagent](module_serving/classification_vgg11_imagenet) - -  该示例展示了利用vgg11_imagent完成图像分类服务化部署和在线预测，获取图像分类结果。 - -* [图像生成-基于stgan_celeba](module_serving/GAN_stgan_celeba) - -  该示例展示了利用stgan_celeba生成图像服务化部署和在线预测，获取指定风格的生成图像。 - -* [文本审核-基于porn_detection_lstm](module_serving/text_censorship_porn_detection_lstm) - -  该示例展示了利用porn_detection_lstm完成中文文本黄色敏感信息鉴定的服务化部署和在线预测，获取文本是否敏感及其置信度。 +预训练模型一键服务部署包括以下示例： * [中文词法分析-基于lac](module_serving/lexical_analysis_lac)   该示例展示了利用lac完成中文文本分词服务化部署和在线预测，获取文本的分词结果，并可通过用户自定义词典干预分词结果。 -* [目标检测-基于yolov3_darknet53_coco2017](module_serving/object_detection_yolov3_darknet53_coco2017) - -  该示例展示了利用yolov3_darknet53_coco2017完成目标检测服务化部署和在线预测，获取检测结果和覆盖识别框的图片。 - -* [中文语义分析-基于simnet_bow](module_serving/semantic_model_simnet_bow) - -  该示例展示了利用simnet_bow完成中文文本相似度检测服务化部署和在线预测，获取文本的相似程度。 - -* [图像分割-基于deeplabv3p_xception65_humanseg](module_serving/semantic_segmentation_deeplabv3p_xception65_humanseg) - -  该示例展示了利用deeplabv3p_xception65_humanseg完成图像分割服务化部署和在线预测，获取识别结果和分割后的图像。 - -* [中文情感分析-基于simnet_bow](module_serving/semantic_model_simnet_bow) - -  该示例展示了利用senta_lstm完成中文文本情感分析服务化部署和在线预测，获取文本的情感分析结果。 +* [人脸检测-基于pyramidbox_lite_server_mask](module_serving/object_detection_pyramidbox_lite_server_mask) -关于Paddle Serving预训练模型一键服务部署功能的具体信息请参见[Module Serving](module_serving)。 +  该示例展示了利用pyramidbox_lite_server_mask完成人脸口罩检测，检测人脸位置以及戴口枣的置信度。 diff --git a/demo/serving/module_serving/GAN_stgan_celeba/README.md b/demo/serving/module_serving/GAN_stgan_celeba/README.md deleted file mode 100644 index efa6c498..00000000 --- a/demo/serving/module_serving/GAN_stgan_celeba/README.md +++ /dev/null @@ -1,84 +0,0 @@ -# 部署图像生成服务-以stgan_celeba为例 -## 简介 -图像生成是指根据预先设置的标签，生成对应图像的过程。stgan_celeba通过在GAN中加入encoder-decoder，可实现人脸属性的转换。关于stgan_celeba的具体信息请参见[stgan_celeba](https://paddlepaddle.org.cn/hubdetail?name=stgan_celeba&en_category=GANs)。 - -使用PaddleHub Serving可以轻松部署一个在线图像生成服务API，可将此API接入自己的web网站，也可接入应用程序，如美图类应用，实现传照片修饰脸的功能。 - -下面就带领大家使用PaddleHub Serving，通过简单几步部署一个图像生成服务。 - -## Step1：启动PaddleHub Serving -启动命令如下： -```shell -$ hub serving start -m stgan_celeba -``` -启动时会显示加载模型过程，启动成功后显示： -```shell -Loading stgan_celeba successful. -``` -这样就完成了一个图像生成服务化API的部署，默认端口号为8866。 - -## Step2：测试图像生成在线API -首先指定编码格式及引入需要的包： -```python -# coding: utf8 -import requests -import json -import base64 -import os -``` -我们用来测试的样例图片为： - -

- -

- -根据stgan_celeba所需信息，准备的数据包括图像文件和生成图像风格，格式为： -```python -files = [("image", file_a), ("image", file_b)] -data = {"info": ["info_a_1, info_a_2", "info_b_1, info_b_2"], "style": ["style_a", "style_b"]} -``` - -**NOTE:** 文件列表每个元素第一个参数为"image"。 - -info为图像描述，根据示例图像信息，info应为"Male,Black_Hair,Eyeglasses,No_Beard"，即"男性，黑发，戴眼镜，没有胡子"。 - -image为要生成的图像风格，我们选取"Bald"(秃顶的)作为生成图像的风格。 - -```python ->>> # 指定要使用的图片文件并生成列表[("image", img_1), ("image", img_2), ... ] ->>> file_list = ["../img/man.png"] ->>> files = [("image", (open(item, "rb"))) for item in file_list] ->>> # 为每张图片对应指定info和style ->>> data = {"info": ["Male,Black_Hair,Eyeglasses,No_Beard"], "style": ["Bald"]} -``` - -## Step3：获取并验证结果 -然后就可以发送请求到图像生成服务API，并得到结果，代码如下： - -```python ->>> url = "http://127.0.0.1:8866/predict/image/stgan_celeba" ->>> r = requests.post(url=url, data=data, files=files) -``` -stgan_celeba返回的结果包括生成图像的base64编码格式，经过转换可以得到生成图像。 - -我们建立一个指定的文件夹用于存放结果图片： -```python ->>> if not os.path.exists("stgan_output"): - os.mkdir("stgan_output") -``` -然后将图片进行保存，代码如下： -```python ->>> for item in results: -... with open(output_path, "wb") as fp: -... fp.write(base64.b64decode(item["base64"].split(',')[-1])) -``` -查看指定输出文件夹，就能看到生成图像了，如图： - -

- -

- - -这样我们就完成了对图像生成服务化的部署和测试。 - -完整的测试代码见[stgan_celeba_serving_demo.py](stgan_celeba_serving_demo.py)。 diff --git a/demo/serving/module_serving/GAN_stgan_celeba/stgan_celeba_serving_demo.py b/demo/serving/module_serving/GAN_stgan_celeba/stgan_celeba_serving_demo.py deleted file mode 100644 index 4e202815..00000000 --- a/demo/serving/module_serving/GAN_stgan_celeba/stgan_celeba_serving_demo.py +++ /dev/null @@ -1,26 +0,0 @@ -# coding: utf8 -import requests -import json -import base64 -import os - -if __name__ == "__main__": - # 指定要使用的图片文件并生成列表[("image", img_1), ("image", img_2), ... ] - file_list = ["../../../../docs/imgs/man.png"] - files = [("image", (open(item, "rb"))) for item in file_list] - # 为每张图片对应指定info和style - data = {"info": ["Male,Black_Hair"], "style": ["Bald"]} - # 指定图片生成方法为stgan_celeba并发送post请求 - url = "http://127.0.0.1:8866/predict/image/stgan_celeba" - r = requests.post(url=url, data=data, files=files) - - # 保存生成的图片到output文件夹，打印模型输出结果 - if not os.path.exists("stgan_output"): - os.mkdir("stgan_output") - results = eval(r.json()["results"]) - for item in results: - output_path = os.path.join("stgan_output", item["path"].split("/")[-1]) - with open(output_path, "wb") as fp: - fp.write(base64.b64decode(item["base64"].split(',')[-1])) - item.pop("base64") - print(json.dumps(results, indent=4, ensure_ascii=False)) diff --git a/demo/serving/module_serving/README.md b/demo/serving/module_serving/README.md index 66ebb05b..5c07d21c 100644 --- a/demo/serving/module_serving/README.md +++ b/demo/serving/module_serving/README.md @@ -14,37 +14,13 @@ PaddleHub Serving是基于PaddleHub的一键模型服务部署工具，能够通 获取PaddleHub Serving的一键服务部署场景示例，可参见下列demo： -* [图像分类-基于vgg11_imagent](../module_serving/classification_vgg11_imagenet) - -  该示例展示了利用vgg11_imagent完成图像分类服务化部署和在线预测，获取图像分类结果。 - -* [图像生成-基于stgan_celeba](../module_serving/GAN_stgan_celeba) - -  该示例展示了利用stgan_celeba生成图像服务化部署和在线预测，获取指定风格的生成图像。 - -* [文本审核-基于porn_detection_lstm](../module_serving/text_censorship_porn_detection_lstm) - -  该示例展示了利用porn_detection_lstm完成中文文本黄色敏感信息鉴定的服务化部署和在线预测，获取文本是否敏感及其置信度。 - * [中文词法分析-基于lac](../module_serving/lexical_analysis_lac)   该示例展示了利用lac完成中文文本分词服务化部署和在线预测，获取文本的分词结果，并可通过用户自定义词典干预分词结果。 -* [目标检测-基于yolov3_darknet53_coco2017](../module_serving/object_detection_yolov3_darknet53_coco2017) - -  该示例展示了利用yolov3_darknet53_coco2017完成目标检测服务化部署和在线预测，获取检测结果和覆盖识别框的图片。 - -* [中文语义分析-基于simnet_bow](../module_serving/semantic_model_simnet_bow) - -  该示例展示了利用simnet_bow完成中文文本相似度检测服务化部署和在线预测，获取文本的相似程度。 - -* [图像分割-基于deeplabv3p_xception65_humanseg](../module_serving/semantic_segmentation_deeplabv3p_xception65_humanseg) - -  该示例展示了利用deeplabv3p_xception65_humanseg完成图像分割服务化部署和在线预测，获取识别结果和分割后的图像。 - -* [中文情感分析-基于senta_lstm](../module_serving/sentiment_analysis_senta_lstm) +* [人脸检测-基于pyramidbox_lite_server_mask](../module_serving/object_detection_pyramidbox_lite_server_mask) -  该示例展示了利用senta_lstm完成中文文本情感分析服务化部署和在线预测，获取文本的情感分析结果。 +  该示例展示了利用pyramidbox_lite_server_mask完成人脸口罩检测，检测人脸位置以及戴口枣的置信度。 ## Bert Service 除了预训练模型一键服务部署功能之外，PaddleHub Serving还具有`Bert Service`功能，支持ernie_tiny、bert等模型快速部署，对外提供可靠的在线embedding服务，具体信息请参见[Bert Service](../../../docs/tutorial/bert_service.md)。 diff --git a/demo/serving/module_serving/classification_vgg11_imagenet/README.md b/demo/serving/module_serving/classification_vgg11_imagenet/README.md deleted file mode 100644 index e82efb98..00000000 --- a/demo/serving/module_serving/classification_vgg11_imagenet/README.md +++ /dev/null @@ -1,76 +0,0 @@ -# 部署图像分类服务-以vgg11_imagenent为例 -## 简介 -图像分类是指通过模型，预测给定的图片所属类别，vgg11_imagenent就是一种有效的图像分类模型。关于vgg11_imagenent的具体信息请参见[vgg11_imagenent](https://paddlepaddle.org.cn/hubdetail?name=vgg11_imagenet&en_category=ImageClassification)。 - -使用PaddleHub Serving可以部署一个在线图片分类服务，既可以对用户暴露直接预测接口，也可以利用此接口实现一个web网站，甚至可以集成到移动端应用程序中实现拍照识别功能。 - -这里就带领大家使用PaddleHub Serving，通过简单几步部署一个图像分类服务。 - -## Step1：启动PaddleHub Serving -启动命令如下： -```shell -$ hub serving start -m vgg11_imagenet -``` -启动时会显示加载模型过程，启动成功后显示： -```shell -Loading vgg11_imagenet successful. -``` -这样就完成了一个图像分类服务化API的部署，默认端口号为8866。 - -## Step2：测试图像分类在线API -首先引入需要的包： -```python ->>> import requests ->>> import json -``` - -我们用来测试的样例图片为： - -

- -

- -

- -

- -准备的数据格式为： -```python -files = [("image", file_1), ("image", file_2)] -``` -**NOTE:** 每个元素第一个参数为"image"。 - -代码如下： -```python ->>> file_list = ["../img/cat.jpg", "../img/flower.jpg"] ->>> files = [("image", (open(item, "rb"))) for item in file_list] -``` - -## Step3：获取并验证结果 -然后就可以发送请求到图像分类服务API，并得到结果了，代码如下： -```python ->>> # 指定检测方法为vgg11_imagenet并发送post请求 ->>> url = "http://127.0.0.1:8866/predict/image/vgg11_imagenet" ->>> r = requests.post(url=url, files=files) -``` -vgg11_imagenent返回的结果为图像分类结果及其对应的概率，我们尝试打印接口返回结果： -```python ->>> results = eval(r.json()["results"]) ->>> print(json.dumps(results, indent=4, ensure_ascii=False)) -[ - [ - { - "Egyptian cat": 0.540287435054779 - } - ], - [ - { - "daisy": 0.9976677298545837 - } - ] -] -``` - -这样我们就完成了对图像分类预测服务化部署和测试。 - -完整的测试代码见[vgg11_imagenent_serving_demo.py](vgg11_imagenet_serving_demo.py)。 diff --git a/demo/serving/module_serving/classification_vgg11_imagenet/vgg11_imagenet_serving_demo.py b/demo/serving/module_serving/classification_vgg11_imagenet/vgg11_imagenet_serving_demo.py deleted file mode 100644 index 091f9517..00000000 --- a/demo/serving/module_serving/classification_vgg11_imagenet/vgg11_imagenet_serving_demo.py +++ /dev/null @@ -1,18 +0,0 @@ -# coding: utf8 -import requests -import json - -if __name__ == "__main__": - # 指定要预测的图片并生成列表[("image", img_1), ("image", img_2), ... ] - file_list = [ - "../../../../docs/imgs/cat.jpg", "../../../../docs/imgs/flower.jpg" - ] - files = [("image", (open(item, "rb"))) for item in file_list] - # 指定预测方法为vgg11_imagenet并发送post请求 - url = "http://127.0.0.1:8866/predict/image/vgg11_imagenet" - r = requests.post(url=url, files=files) - - results = eval(r.json()["results"]) - - # 打印预测结果 - print(json.dumps(results, indent=4, ensure_ascii=False)) diff --git a/demo/serving/module_serving/lexical_analysis_lac/lac_2.1.0_serving_demo.py b/demo/serving/module_serving/lexical_analysis_lac/lac_2.1.0_serving_demo.py deleted file mode 100644 index 05e40f8a..00000000 --- a/demo/serving/module_serving/lexical_analysis_lac/lac_2.1.0_serving_demo.py +++ /dev/null @@ -1,19 +0,0 @@ -# coding: utf8 -import requests -import json - -if __name__ == "__main__": - # 指定用于预测的文本并生成字典{"text": [text_1, text_2, ... ]} - text = ["今天是个好日子", "天气预报说今天要下雨"] - # 以key的方式指定text传入预测方法的时的参数，此例中为"data" - # 对应本地部署，则为lac.analysis_lexical(data=text, batch_size=1) - data = {"texts": text, "batch_size": 1} - # 指定预测方法为lac并发送post请求，content-type类型应指定json方式 - url = "http://127.0.0.1:8866/predict/lac" - # 指定post请求的headers为application/json方式 - headers = {"Content-Type": "application/json"} - - r = requests.post(url=url, headers=headers, data=json.dumps(data)) - - # 打印预测结果 - print(json.dumps(r.json(), indent=4, ensure_ascii=False)) diff --git a/demo/serving/module_serving/lexical_analysis_lac/lac_serving_demo.py b/demo/serving/module_serving/lexical_analysis_lac/lac_serving_demo.py index 58d696bc..05e40f8a 100644 --- a/demo/serving/module_serving/lexical_analysis_lac/lac_serving_demo.py +++ b/demo/serving/module_serving/lexical_analysis_lac/lac_serving_demo.py @@ -4,11 +4,16 @@ import json if __name__ == "__main__": # 指定用于预测的文本并生成字典{"text": [text_1, text_2, ... ]} - text_list = ["今天是个好日子", "天气预报说今天要下雨"] - text = {"text": text_list} - # 指定预测方法为lac并发送post请求 - url = "http://127.0.0.1:8866/predict/text/lac" - r = requests.post(url=url, data=text) + text = ["今天是个好日子", "天气预报说今天要下雨"] + # 以key的方式指定text传入预测方法的时的参数，此例中为"data" + # 对应本地部署，则为lac.analysis_lexical(data=text, batch_size=1) + data = {"texts": text, "batch_size": 1} + # 指定预测方法为lac并发送post请求，content-type类型应指定json方式 + url = "http://127.0.0.1:8866/predict/lac" + # 指定post请求的headers为application/json方式 + headers = {"Content-Type": "application/json"} + + r = requests.post(url=url, headers=headers, data=json.dumps(data)) # 打印预测结果 print(json.dumps(r.json(), indent=4, ensure_ascii=False)) diff --git a/demo/serving/module_serving/lexical_analysis_lac/lac_with_dict_serving_demo.py b/demo/serving/module_serving/lexical_analysis_lac/lac_with_dict_serving_demo.py deleted file mode 100644 index 94446388..00000000 --- a/demo/serving/module_serving/lexical_analysis_lac/lac_with_dict_serving_demo.py +++ /dev/null @@ -1,16 +0,0 @@ -# coding: utf8 -import requests -import json - -if __name__ == "__main__": - # 指定用于预测的文本并生成字典{"text": [text_1, text_2, ... ]} - text_list = ["今天是个好日子", "天气预报说今天要下雨"] - text = {"text": text_list} - # 指定自定义词典{"user_dict": dict.txt} - file = {"user_dict": open("dict.txt", "rb")} - # 指定预测方法为lac并发送post请求 - url = "http://127.0.0.1:8866/predict/text/lac" - r = requests.post(url=url, files=file, data=text) - - # 打印预测结果 - print(json.dumps(r.json(), indent=4, ensure_ascii=False)) diff --git a/demo/serving/module_serving/object_detection_pyramidbox_lite_server_mask/README.md b/demo/serving/module_serving/object_detection_pyramidbox_lite_server_mask/README.md index f5c552b2..1e7e1c1b 100644 --- a/demo/serving/module_serving/object_detection_pyramidbox_lite_server_mask/README.md +++ b/demo/serving/module_serving/object_detection_pyramidbox_lite_server_mask/README.md @@ -29,7 +29,7 @@ Loading pyramidbox_lite_server_mask successful. >>> import requests >>> import json >>> import base64 ->>> import os +>>> import cv2 ``` 我们用来测试的样例图片为： @@ -47,23 +47,18 @@ Loading pyramidbox_lite_server_mask successful. 准备的数据格式为： ```python -files = [("image", file_1), ("image", file_2)] -``` -**NOTE:** 文件列表每个元素第一个参数为"image"。 - -代码如下： -```python ->>> # 指定要检测的图片并生成列表[("image", img_1), ("image", img_2), ... ] ->>> file_list = ["../../../../docs/imgs/family_mask.jpg", "../../../../docs/imgs/girl_mask.jpg"] ->>> files = [("image", (open(item, "rb"))) for item in file_list] +>>> img1 = cv2_to_base64(cv2.imread("../../../../docs/imgs/family_mask.jpg")) +>>> img2 = cv2_to_base64(cv2.imread("../../../../docs/imgs/woman_mask.jpg")) +>>> data = {'images': [img1, img2]} ``` ## Step3：获取并验证结果 通过发送请求到目标检测服务API，就可得到结果，代码如下： ```python >>> # 指定检测方法为pyramidbox_lite_server_mask并发送post请求 ->>> url = "http://127.0.0.1:8866/predict/image/pyramidbox_lite_server_mask" ->>> r = requests.post(url=url, files=files, data={"visual_result": "True"}) +>>> headers = {"Content-type": "application/json"} +>>> url = "http://127.0.0.1:8866/predict/pyramidbox_lite_server_mask" +>>> r = requests.post(url=url, headers=headers, data=json.dumps(data)) ``` 我们可以打印接口返回结果： ```python @@ -165,24 +160,4 @@ pyramidbox_lite_server_mask返回的结果还包括标注检测框的图像的ba 这样我们就完成了对目标检测服务化的部署和测试。 -完整的测试代码见[pyramidbox_lite_server_mask_file_serving_demo.py](pyramidbox_lite_server_mask_file_serving_demo.py)。 - -## 进一步提升模型服务性能 -`pyramidbox_lite_server_mask`还支持直接传入opencv mat表示的图片，不产生结果文件，而是直接输出检测的人脸位置和戴口罩概率，响应时间平均提升20%以上，可用于对响应时间和性能要求更高的场景。 - -使用直接传输数据的模式，仅需要修改上文Step2中的POST方法参数，具体如下： - -```python ->>> with open(file="../../../../docs/imgs/family_mask.jpg", mode="rb") as fp: -... base64_data = base64.b64encode(fp.read()) ->>> base64_data = str(base64_data, encoding="utf8") ->>> data = {"b64s": [base64_data]} ->>> data = {"data": json.dumps(data)} -``` -进行HTTP请求时只需将data参数传入即可，具体如下： -```python ->>> r = requests.post(url=url, data=data) -``` -对结果的处理与上文一致，但需注意此种方法仅输出识别结果，不产生结果文件，因此不能获得生成图片。 - -完整的测试代码见[pyramidbox_lite_server_mask_serving_demo.py](pyramidbox_lite_server_mask_serving_demo.py)。 +完整的测试代码见[pyramidbox_lite_server_mask_file_serving_demo.py](pyramidbox_lite_server_mask_serving_demo.py)。 diff --git a/demo/serving/module_serving/object_detection_pyramidbox_lite_server_mask/pyramidbox_lite_server_mask_file_serving_demo.py b/demo/serving/module_serving/object_detection_pyramidbox_lite_server_mask/pyramidbox_lite_server_mask_file_serving_demo.py deleted file mode 100644 index 0096d2a8..00000000 --- a/demo/serving/module_serving/object_detection_pyramidbox_lite_server_mask/pyramidbox_lite_server_mask_file_serving_demo.py +++ /dev/null @@ -1,28 +0,0 @@ -# coding: utf8 -import requests -import json -import base64 -import os - -if __name__ == "__main__": - file_list = [ - "../../../../docs/imgs/family_mask.jpg", - "../../../../docs/imgs/woman_mask.jpg" - ] - files = [("image", (open(item, "rb"))) for item in file_list] - - # 指定检测方法为pyramidbox_lite_server_mask并发送post请求 - url = "http://127.0.0.1:8866/predict/image/pyramidbox_lite_server_mask" - r = requests.post(url=url, files=files, data={"visual_result": "True"}) - - # 创建图片保存文件夹 - if not os.path.exists("output"): - os.mkdir("output") - - results = eval(r.json()["results"]) - for item in results: - with open(os.path.join("output", item["path"]), "wb") as fp: - fp.write(base64.b64decode(item["base64"].split(',')[-1])) - item.pop("base64") - - print(json.dumps(results, indent=4, ensure_ascii=False)) diff --git a/demo/serving/module_serving/object_detection_pyramidbox_lite_server_mask/pyramidbox_lite_server_mask_serving_demo.py b/demo/serving/module_serving/object_detection_pyramidbox_lite_server_mask/pyramidbox_lite_server_mask_serving_demo.py index 4ee6f916..955233ec 100644 --- a/demo/serving/module_serving/object_detection_pyramidbox_lite_server_mask/pyramidbox_lite_server_mask_serving_demo.py +++ b/demo/serving/module_serving/object_detection_pyramidbox_lite_server_mask/pyramidbox_lite_server_mask_serving_demo.py @@ -1,26 +1,25 @@ # coding: utf8 import requests import json +import cv2 import base64 -if __name__ == "__main__": - # 获取第一张图片文件的base64编码 - with open(file="../../../../docs/imgs/family_mask.jpg", mode="rb") as fp: - base64_data1 = base64.b64encode(fp.read()) - base64_data1 = str(base64_data1, encoding="utf8") - # 获取第二张图片文件的base64编码 - with open(file="../../../../docs/imgs/woman_mask.jpg", mode="rb") as fp: - base64_data2 = base64.b64encode(fp.read()) - base64_data2 = str(base64_data2, encoding="utf8") - data = {"b64s": [base64_data1, base64_data2]} - data = {"data": json.dumps(data)} +def cv2_to_base64(image): + data = cv2.imencode('.jpg', image)[1] + return base64.b64encode(data.tostring()).decode('utf8') - # 指定检测方法为pyramidbox_lite_server_mask并发送post请求 - url = "http://127.0.0.1:8866/predict/image/pyramidbox_lite_server_mask" - r = requests.post(url=url, data=data) - # 得到并打印检测结果 - results = eval(r.json()["results"]) +if __name__ == '__main__': + # 获取图片的base64编码格式 + img1 = cv2_to_base64(cv2.imread("../../../../docs/imgs/family_mask.jpg")) + img2 = cv2_to_base64(cv2.imread("../../../../docs/imgs/woman_mask.jpg")) + data = {'images': [img1, img2]} + # 指定content-type + headers = {"Content-type": "application/json"} + # 发送HTTP请求 + url = "http://127.0.0.1:8866/predict/pyramidbox_lite_server_mask" + r = requests.post(url=url, headers=headers, data=json.dumps(data)) - print(json.dumps(results, indent=4, ensure_ascii=False)) + # 打印预测结果 + print(r.json()["results"]) diff --git a/demo/serving/module_serving/object_detection_yolov3_darknet53_coco2017/README.md b/demo/serving/module_serving/object_detection_yolov3_darknet53_coco2017/README.md deleted file mode 100644 index 7893fa7b..00000000 --- a/demo/serving/module_serving/object_detection_yolov3_darknet53_coco2017/README.md +++ /dev/null @@ -1,135 +0,0 @@ -# 部署图像分类服务-以yolov3_darknet53_coco2017为例 -## 简介 -目标检测作为深度学习常见任务，在各种场景下都有所使用。使用`yolov3_darknet53_coco2017`模型可以进行目标检测任务，关于`yolov3_darknet53_coco2017`的具体信息请参见[yolov3_darknet53_coco2017](https://paddlepaddle.org.cn/hubdetail?name=yolov3_darknet53_coco2017&en_category=ObjectDetection)。 - -使用PaddleHub Serving可以轻松部署一个在线目标检测服务API，可将此API接入自己的web网站进行在线目标检测，也可接入移动端应用程序，实现识图、圈人等功能。 - -下面就带领大家使用PaddleHub Serving，通过简单几步部署一个目标检测服务。 - -## Step1：启动PaddleHub Serving -启动命令如下： -```shell -$ hub serving start -m yolov3_darknet53_coco2017 -``` -启动时会显示加载模型过程，启动成功后显示： -```shell -Loading yolov3_darknet53_coco2017 successful. -``` -这样就完成了一个图像生成服务化API的部署，默认端口号为8866。 - -## Step2：测试图像生成在线API -首先引入需要的包： -```python ->>> import requests ->>> import json ->>> import base64 ->>> import os -``` -我们用来测试的样例图片为： - -

- - - -

- -

- - - -

- -准备的数据格式为： -```python -files = [("image", file_1), ("image", file_2)] -``` -**NOTE:** 文件列表每个元素第一个参数为"image"。 - -代码如下： -```python ->>> # 指定要检测的图片并生成列表[("image", img_1), ("image", img_2), ... ] ->>> file_list = ["../img/cat.jpg", "../img/dog.jpg"] ->>> files = [("image", (open(item, "rb"))) for item in file_list] -``` - -## Step3：获取并验证结果 -然后就可以发送请求到目标检测服务API，并得到结果，代码如下： -```python ->>> # 指定检测方法为yolov3_darknet53_coco2017并发送post请求 ->>> url = "http://127.0.0.1:8866/predict/image/yolov3_darknet53_coco2017" ->>> r = requests.post(url=url, files=files) -``` -我们可以打印接口返回结果： -```python ->>> results = eval(r.json()["results"]) ->>> print(json.dumps(results, indent=4, ensure_ascii=False)) -[ - { - "path": "cat.jpg", - "data": [ - { - "left": 319.489, - "right": 1422.8364, - "top": 208.94229, - "bottom": 993.8552, - "label": "cat", - "confidence": 0.9174191 - } - ] - }, - { - "path": "dog.jpg", - "data": [ - { - "left": 200.6918, - "right": 748.96204, - "top": 122.74927, - "bottom": 566.2066, - "label": "dog", - "confidence": 0.83619183 - }, - { - "left": 506.8462, - "right": 623.2322, - "top": 378.0084, - "bottom": 416.116, - "label": "tie", - "confidence": 0.5082839 - } - ] - } -] -``` -根据结果可以看出准确识别了请求的图片。 - -yolov3_darknet53_coco2017返回的结果还包括标注检测框的图像的base64编码格式，经过转换可以得到生成图像。 -我们创建一个保存结果图片的文件夹： -```python ->>> if not os.path.exists("output"): ->>> os.mkdir("output") -``` -然后将图片数据进行解码并保存，代码如下： -```python ->>> results = eval(r.json()["results"]) ->>> for item in results: -... with open(output_path, "wb") as fp: -... fp.write(base64.b64decode(item["base64"].split(',')[-1])) -``` -查看指定输出文件夹，就能看到生成图像了，如图： - -

- - - -

- -

- - - -

- - -这样我们就完成了对目标检测服务化的部署和测试。 - -完整的测试代码见[yolov3_darknet53_coco2017_serving_demo.py](yolov3_darknet53_coco2017_serving_demo.py)。 diff --git a/demo/serving/module_serving/object_detection_yolov3_darknet53_coco2017/output/cat.jpg b/demo/serving/module_serving/object_detection_yolov3_darknet53_coco2017/output/cat.jpg deleted file mode 100644 index d71260a8c364349bc0f07ef9938220eef893d857..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 128218 zcmce82|QHa8}}V!&ypI2EZH)nBpH%0BiXaeSc;UQ82nNaqGd+fOhWdh5Mxh8R4UP8 zt88Pbkc#Yy?CW^X9ZTx>|9{@)^S*C=u3^r(_nz~d=lMS0=ef_h^B?EGLaUAR4D}!c z0)i0WKQ!M0=|G(9>>TWDoE#h+TwI*oJSbkCRjYV}*R177iHS&vi;0MeZj{+7w^3?~ zw5TY02WHDQB`g*zA-7XgZM()+W$bqNA_y)nE}m69LcF{}+a*OMxBqYdn=gR`I9b)$ zl8^`~h(!Q_6hO?sfh52<*$~Ws(BeM?3zC(Mor9B$dll$Vuo_}PAdxJrNH#WBR?z!A zXopw@*w#oYX|t~-9O01iKyCNGd52SackW9;V%?C8vcsv1T-@u{3ki$JZjwV|+I3h(>E|QvaqzW-nZYz*74{uCufq2tLJGiZ=W;1egT&*U%7fMkQx>q5g8R7 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zMx-p}&Rmr)eLyi_KR%QEgixxJPV-5qeaAYQH(ohX(~(QdCTS-#_C5~dkZK6^efbl~ z#oI)YkDTHn(qo-jlybnY+l-~5&)yL}Ytf&-O3gCv2#*xjUaeSFe8r{XMgh5)W;p^H zzFZ*G`Or)VoDFd2UT2aja4Oz`F36q{Elt%)O_g-U`Onc{rvRtmi`ZMWnn$Y0@G*pz6^U3_bx(T)o& R)FzTEp|V$5HSK>> # coding: utf8 ->>> import requests ->>> import json -``` -准备的数据格式为： -```python -{"text_1": [text_a1, text_a2, ... ], "text_2": [text_b1, text_b2, ... ]} -``` -**NOTE:** 字典的key分别为"text_1"和"text_2"，与`simnet_bow`模型使用的输入数据一致。 - -根据文本和数据格式，代码如下： -```python ->>> # 指定用于用于匹配的文本并生成字典{"text_1": [text_a1, text_a2, ... ] ->>> # "text_2": [text_b1, text_b2, ... ]} ->>> text = { ->>> "text_1": ["这道题太难了", "这道题太难了", "这道题太难了"], ->>> "text_2": ["这道题是上一年的考题", "这道题不简单", "这道题很有意思"] ->>> } -``` - -## Step3：获取并验证结果 -接下来发送请求到语义模型API，并得到结果，代码如下： -```python ->>> # 指定匹配方法为simnet_bow并发送post请求 ->>> url = "http://127.0.0.1:8866/predict/text/simnet_bow" ->>> r = requests.post(url=url, data=text) -``` -`simnet_bow`模型返回的结果为每对文本对比后的相似度，我们尝试打印接口返回结果： -```python -# 打印预测结果 ->>> print(json.dumps(r.json(), indent=4, ensure_ascii=False)) -{ - "msg": "", - "results": [ - { - "similarity": 0.8445, - "text_1": "这道题太难了", - "text_2": "这道题是上一年的考题" - }, - { - "similarity": 0.9275, - "text_1": "这道题太难了", - "text_2": "这道题不简单" - }, - { - "similarity": 0.9083, - "text_1": "这道题太难了", - "text_2": "这道题很有意思" - } - ], - "status": "0" -} -``` -这样我们就完成了对语义模型simnet_bow的预测服务化部署和测试。 - -完整的测试代码见[simnet_bow_serving_demo.py](simnet_bow_serving_demo.py)。 diff --git a/demo/serving/module_serving/semantic_model_simnet_bow/simnet_bow_serving_demo.py b/demo/serving/module_serving/semantic_model_simnet_bow/simnet_bow_serving_demo.py deleted file mode 100644 index 73d38e81..00000000 --- a/demo/serving/module_serving/semantic_model_simnet_bow/simnet_bow_serving_demo.py +++ /dev/null @@ -1,17 +0,0 @@ -# coding: utf8 -import requests -import json - -if __name__ == "__main__": - # 指定用于匹配的文本并生成字典{"text_1": [text_a1, text_a2, ... ] - # "text_2": [text_b1, text_b2, ... ]} - text = { - "text_1": ["这道题太难了", "这道题太难了", "这道题太难了"], - "text_2": ["这道题是上一年的考题", "这道题不简单", "这道题很有意思"] - } - # 指定匹配方法为simnet_bow并发送post请求 - url = "http://127.0.0.1:8866/predict/text/simnet_bow" - r = requests.post(url=url, data=text) - - # 打印匹配结果 - print(json.dumps(r.json(), indent=4, ensure_ascii=False)) diff --git a/demo/serving/module_serving/semantic_segmentation_deeplabv3p_xception65_humanseg/README.md b/demo/serving/module_serving/semantic_segmentation_deeplabv3p_xception65_humanseg/README.md deleted file mode 100644 index 1b5a2aab..00000000 --- a/demo/serving/module_serving/semantic_segmentation_deeplabv3p_xception65_humanseg/README.md +++ /dev/null @@ -1,91 +0,0 @@ -# 部署图像分割服务-以deeplabv3p_xception65_humanseg为例 -## 简介 -图像分割是深度学习的常见任务。使用`deeplabv3p_xception65_humanseg`模型可以进行人像分割任务，关于`deeplabv3p_xception65_humanseg`的具体信息请参见[deeplabv3p_xception65_humanseg](https://paddlepaddle.org.cn/hubdetail?name=deeplabv3p_xception65_humanseg&en_category=ImageSegmentation)。 - -使用PaddleHub Serving可以轻松部署一个在线图像分割服务API，可将此API接入自己的web网站进行在线图像分割，也可接入移动端应用程序，实现拍照分割等功能。 - -下面就带领大家使用PaddleHub Serving，通过简单几步部署一个目标检测服务。 - -## Step1：启动PaddleHub Serving -启动命令如下 -```shell -$ hub serving start -m deeplabv3p_xception65_humanseg -``` -启动时会显示加载模型过程，启动成功后显示： -```shell -Loading deeplabv3p_xception65_humanseg successful. -``` -这样就完成了一个图像分割服务化API的部署，默认端口号为8866。 - -## Step2：测试图像分割在线API -首先指定编码格式及引入需要的包： -```python ->>> # coding: utf8 ->>> import requests ->>> import json ->>> import base64 ->>> import os -``` -我们用来测试的样例图片为： - -

- - - -

- -准备的数据格式为： -```python -files = [("image", file_1), ("image", file_2)] -``` -**NOTE:** 文件列表每个元素第一个参数为"image"。 - -代码如下 -```python ->>> # 指定要检测的图片并生成列表[("image", img_1), ("image", img_2), ... ] ->>> file_list = ["../../../../docs/imgs/girl.jpg"] ->>> files = [("image", (open(item, "rb"))) for item in file_list] -``` - -## Step3：获取并验证结果 -然后就可以发送请求到图像分割服务API，并得到结果，代码如下： -```python ->>> # 指定检测方法为deeplabv3p_xception65_humanseg并发送post请求 ->>> url = "http://127.0.0.1:8866/predict/image/deeplabv3p_xception65_humanseg" ->>> r = requests.post(url=url, files=files) -``` -我们可以打印接口返回结果： -```python ->>> results = eval(r.json()["results"]) ->>> print(json.dumps(results, indent=4, ensure_ascii=False)) -[ - { - "origin": "girl.jpg", - "processed": "humanseg_output/girl.png" - } -] -``` -deeplabv3p_xception65_humanseg返回的结果还包括人像分割后的图像的base64编码格式，经过转换可以得到生成图像。 -我们建立一个文件夹用于存放结果图片： -```python ->>> if not os.path.exists("output"): ->>> os.mkdir("output") -``` -然后将图片数据解码并保存，代码如下： -```python ->>> results = eval(r.json()["results"]) ->>> for item in results: -... with open(output_path, "wb") as fp: -... fp.write(base64.b64decode(item["base64"].split(',')[-1])) -``` -查看指定输出文件夹，就能看到生成图像了，如图： - -

- - - -

- -这样我们就完成了对图像分割模型deeplabv3p_xception65_humanseg服务化的部署和测试。 - -完整的测试代码见[deeplabv3p_xception65_humanseg_serving_demo.py](deeplabv3p_xception65_humanseg_serving_demo.py)。 diff --git a/demo/serving/module_serving/semantic_segmentation_deeplabv3p_xception65_humanseg/deeplabv3p_xception65_humanseg_serving_demo.py b/demo/serving/module_serving/semantic_segmentation_deeplabv3p_xception65_humanseg/deeplabv3p_xception65_humanseg_serving_demo.py deleted file mode 100644 index 77b80754..00000000 --- a/demo/serving/module_serving/semantic_segmentation_deeplabv3p_xception65_humanseg/deeplabv3p_xception65_humanseg_serving_demo.py +++ /dev/null @@ -1,26 +0,0 @@ -# coding: utf8 -import requests -import json -import base64 -import os - -if __name__ == "__main__": - # 指定要使用的图片文件并生成列表[("image", img_1), ("image", img_2), ... ] - file_list = ["../../../../docs/imgs/girl.jpg"] - files = [("image", (open(item, "rb"))) for item in file_list] - # 指定图片分割方法为deeplabv3p_xception65_humanseg并发送post请求 - url = "http://127.0.0.1:8866/predict/image/deeplabv3p_xception65_humanseg" - r = requests.post(url=url, files=files) - - # 保存分割后的图片到output文件夹，打印模型输出结果 - if not os.path.exists("output"): - os.mkdir("output") - - results = eval(r.json()["results"]) - for item in results: - with open( - os.path.join("output", item["processed"].split("/")[-1]), - "wb") as fp: - fp.write(base64.b64decode(item["base64"].split(',')[-1])) - item.pop("base64") - print(json.dumps(results, indent=4, ensure_ascii=False)) diff --git a/demo/serving/module_serving/sentiment_analysis_senta_lstm/README.md b/demo/serving/module_serving/sentiment_analysis_senta_lstm/README.md deleted file mode 100644 index 62c9c5c2..00000000 --- a/demo/serving/module_serving/sentiment_analysis_senta_lstm/README.md +++ /dev/null @@ -1,78 +0,0 @@ -# 部署情感分析服务-以senta_lstm为例 -## 简介 -情感分析针对带有主观描述的中文文本，可自动判断该文本的情感极性类别并给出相应的置信度。利用`senta_lstm`模型可以完成中文情感分析任务，关于`senta_lstm`的具体信息请参见[senta_lstm] -(https://paddlepaddle.org.cn/hubdetail?name=senta_lstm&en_category=SentimentAnalysis)。 - -使用PaddleHub Serving可以部署一个在线情感分析服务，可以将此接口用于分析评论、智能客服等应用。 - -这里就带领大家使用PaddleHub Serving，通过简单几步部署一个情感分析在线服务。 - -## Step1：启动PaddleHub Serving -启动命令如下 -```shell -$ hub serving start -m senta_lstm -``` -启动时会显示加载模型过程，启动成功后显示 -```shell -Loading senta_lstm successful. -``` -这样就完成了一个词法分析服务化API的部署，默认端口号为8866。 - -## Step2：测试词法分析在线API -在服务部署好之后，我们可以进行测试。 - -首先指定编码格式及引入需要的包： -```python ->>> # coding: utf8 ->>> import requests ->>> import json -``` - -用来测试的文本为`我不爱吃甜食`和`我喜欢躺在床上看电影`，准备的数据格式为： -```python -{"text": [text_1, text_2, ...]} -``` -**NOTE:** 字典的key为"text"。 - -根据文本和数据格式，代码如下： -```python ->>> # 指定用于用于预测的文本并生成字典{"text": [text_1, text_2, ... ]} ->>> text_list = ["我不爱吃甜食", "我喜欢躺在床上看电影"] ->>> text = {"text": text_list} -``` - -## Step3：获取并验证结果 -接下来发送请求到词法分析API，并得到结果，代码如下： -```python -# 指定预测方法为senta_lstm并发送post请求 ->>> url = "http://127.0.0.1:8866/predict/text/senta_lstm" ->>> r = requests.post(url=url, data=text) -``` -我们尝试打印接口返回结果： -```python -# 打印预测结果 ->>> print(json.dumps(r.json(), indent=4, ensure_ascii=False)) -{ - "msg": "", - "results": [ - { - "negative_probs": 0.7079, - "positive_probs": 0.2921, - "sentiment_key": "negative", - "sentiment_label": 0, - "text": "我不爱吃甜食" - }, - { - "negative_probs": 0.0149, - "positive_probs": 0.9851, - "sentiment_key": "positive", - "sentiment_label": 1, - "text": "我喜欢躺在床上看电影" - } - ], - "status": "0" -} -``` -这样我们就完成了对词法分析的预测服务化部署和测试。 - -完整的测试代码见[senta_lstm_serving_demo.py](senta_lstm_serving_demo.py)。 diff --git a/demo/serving/module_serving/sentiment_analysis_senta_lstm/senta_lstm_serving_demo.py b/demo/serving/module_serving/sentiment_analysis_senta_lstm/senta_lstm_serving_demo.py deleted file mode 100644 index 9ff7f49c..00000000 --- a/demo/serving/module_serving/sentiment_analysis_senta_lstm/senta_lstm_serving_demo.py +++ /dev/null @@ -1,14 +0,0 @@ -# coding: utf8 -import requests -import json - -if __name__ == "__main__": - # 指定用于预测的文本并生成字典{"text": [text_1, text_2, ... ]} - text_list = ["我不爱吃甜食", "我喜欢躺在床上看电影"] - text = {"text": text_list} - # 指定预测方法为senta_lstm并发送post请求 - url = "http://127.0.0.1:8866/predict/text/senta_lstm" - r = requests.post(url=url, data=text) - - # 打印预测结果 - print(json.dumps(r.json(), indent=4, ensure_ascii=False)) diff --git a/demo/serving/module_serving/text_censorship_porn_detection_lstm/README.md b/demo/serving/module_serving/text_censorship_porn_detection_lstm/README.md deleted file mode 100644 index ea0a3135..00000000 --- a/demo/serving/module_serving/text_censorship_porn_detection_lstm/README.md +++ /dev/null @@ -1,79 +0,0 @@ -# 部署文本审核服务-以porn_detection_lstm为例 -## 简介 -在网站建设等场景中经常需要对敏感信息进行鉴定和过滤，采用文本审核模型`porn_detection_lstm`可自动判别文本是否涉黄并给出相应的置信度，关于`porn_detection_lstm`的具体信息请参见[porn_detection_lstm](https://paddlepaddle.org -.cn/hubdetail?name=porn_detection_lstm&en_category=TextCensorship) - -使用PaddleHub Serving可以部署一个在线文本审核服务，可以将此接口用于防止低俗交友、色情文本等应用。 - -这里就带领大家使用PaddleHub Serving，通过简单几步部署一个文本审核在线服务。 - -## Step1：启动PaddleHub Serving -启动命令如下： -```shell -$ hub serving start -m porn_detection_lstm -``` -启动时会显示加载模型过程，启动成功后显示： -```shell -Loading porn_detection_lstm successful. -``` -这样就完成了一个文本审核服务化API的部署，默认端口号为8866。 - -## Step2：测试文本审核在线API -在服务部署好之后，我们可以进行测试。 - -首先指定编码格式及引入需要的包： -```python ->>> # coding: utf8 ->>> import requests ->>> import json -``` - -用来测试的文本为`黄片下载`和`中国黄页`，准备的数据格式为： -```python -{"text": [text_1, text_2, ...]} -``` -**NOTE:** 字典的key为"text"。 - -根据文本和数据格式，代码如下： -```python ->>> # 指定用于用于预测的文本并生成字典{"text": [text_1, text_2, ... ]} ->>> text_list = ["黄片下载", "中国黄页"] ->>> text = {"text": text_list} -``` -## Step3：获取并验证结果 -接下来发送请求到文本审核API，并得到结果，代码如下： -```python -# 指定预测方法为lac并发送post请求 ->>> url = "http://127.0.0.1:8866/predict/text/porn_detection_lstm" ->>> r = requests.post(url=url, data=text) -``` -`porn_detection_lstm`模型返回的结果为每个文本鉴定后的结果，我们尝试打印接口返回结果： -```python -# 打印预测结果 ->>> print(json.dumps(r.json(), indent=4, ensure_ascii=False)) -{ - "msg": "", - "results": [ - { - "not_porn_probs": 0.0121, - "porn_detection_key": "porn", - "porn_detection_label": 1, - "porn_probs": 0.9879, - "text": "黄片下载" - }, - { - "not_porn_probs": 0.9954, - "porn_detection_key": "not_porn", - "porn_detection_label": 0, - "porn_probs": 0.0046, - "text": "中国黄页" - } - ], - "status": "0" -} -``` -可以看出正确得到了两个文本的预测结果。 - -这样我们就完成了对文本审核模型的预测服务化部署和测试。 - -完整的测试代码见[porn_detection_lstm_serving_demo.py](porn_detection_lstm_serving_demo.py)。 diff --git a/demo/serving/module_serving/text_censorship_porn_detection_lstm/porn_detection_lstm_serving_demo.py b/demo/serving/module_serving/text_censorship_porn_detection_lstm/porn_detection_lstm_serving_demo.py deleted file mode 100644 index 627f3d9b..00000000 --- a/demo/serving/module_serving/text_censorship_porn_detection_lstm/porn_detection_lstm_serving_demo.py +++ /dev/null @@ -1,14 +0,0 @@ -# coding: utf8 -import requests -import json - -if __name__ == "__main__": - # 指定用于预测的文本并生成字典{"text": [text_1, text_2, ... ]} - text_list = ["黄片下载", "中国黄页"] - text = {"text": text_list} - # 指定预测方法为lac并发送post请求 - url = "http://127.0.0.1:8866/predict/text/porn_detection_lstm" - r = requests.post(url=url, data=text) - - # 打印预测结果 - print(json.dumps(r.json(), indent=4, ensure_ascii=False)) diff --git a/docs/tutorial/serving.md b/docs/tutorial/serving.md index cbaf2b7f..80af3ae1 100644 --- a/docs/tutorial/serving.md +++ b/docs/tutorial/serving.md @@ -82,9 +82,9 @@ $ hub serving start --config config.json 在使用PaddleHub Serving部署服务端的模型预测服务后，就可以在客户端访问预测接口以获取结果了，接口url格式为： -http://127.0.0.1:8866/predict//\ +`http://127.0.0.1:8866/predict/` -其中，\为text或image，与模型种类对应，\为模型名。 +其中，``为模型名。 通过发送一个POST请求，即可获取预测结果，下面我们将展示一个具体的demo，以说明使用PaddleHub Serving部署和使用流程。 @@ -163,12 +163,17 @@ import requests import json if __name__ == "__main__": - # 指定用于用于预测的文本并生成字典{"text": [text_1, text_2, ... ]} - text_list = ["今天是个好日子", "天气预报说今天要下雨"] - text = {"text": text_list} - # 指定预测方法为lac并发送post请求 - url = "http://127.0.0.1:8866/predict/text/lac" - r = requests.post(url=url, data=text) + # 指定用于预测的文本并生成字典{"text": [text_1, text_2, ... ]} + text = ["今天是个好日子", "天气预报说今天要下雨"] + # 以key的方式指定text传入预测方法的时的参数，此例中为"data" + # 对应本地部署，则为lac.analysis_lexical(data=text, batch_size=1) + data = {"texts": text, "batch_size": 1} + # 指定预测方法为lac并发送post请求，content-type类型应指定json方式 + url = "http://127.0.0.1:8866/predict/lac" + # 指定post请求的headers为application/json方式 + headers = {"Content-Type": "application/json"} + + r = requests.post(url=url, headers=headers, data=json.dumps(data)) # 打印预测结果 print(json.dumps(r.json(), indent=4, ensure_ascii=False)) @@ -178,6 +183,7 @@ if __name__ == "__main__": ```python { + "msg": "", "results": [ { "tag": [ @@ -195,8 +201,10 @@ if __name__ == "__main__": "天气预报", "说", "今天", "要", "下雨" ] } - ] + ], + "status": "0" } + ``` ### Step3：停止serving服务 @@ -219,72 +227,13 @@ $ PaddleHub Serving will stop. ## Demo——其他模型的一键部署服务 -获取其他PaddleHub Serving的一键服务部署场景示例，可参见下列demo - -* [图像分类-基于vgg11_imagent](../../demo/serving/module_serving/classification_vgg11_imagenet) - -  该示例展示了利用vgg11_imagent完成图像分类服务化部署和在线预测，获取图像分类结果。 - -* [图像生成-基于stgan_celeba](../../demo/serving/module_serving/GAN_stgan_celeba) - -  该示例展示了利用stgan_celeba生成图像服务化部署和在线预测，获取指定风格的生成图像。 - -* [文本审核-基于porn_detection_lstm](../../demo/serving/module_serving/text_censorship_porn_detection_lstm) - -  该示例展示了利用porn_detection_lstm完成中文文本黄色敏感信息鉴定的服务化部署和在线预测，获取文本是否敏感及其置信度。 - * [中文词法分析-基于lac](../../demo/serving/module_serving/lexical_analysis_lac)   该示例展示了利用lac完成中文文本分词服务化部署和在线预测，获取文本的分词结果，并可通过用户自定义词典干预分词结果。 -* [目标检测-基于yolov3_darknet53_coco2017](../../demo/serving/module_serving/object_detection_yolov3_darknet53_coco2017) - -  该示例展示了利用yolov3_darknet53_coco2017完成目标检测服务化部署和在线预测，获取检测结果和覆盖识别框的图片。 - -* [中文语义分析-基于simnet_bow](../../demo/serving/module_serving/semantic_model_simnet_bow) - -  该示例展示了利用simnet_bow完成中文文本相似度检测服务化部署和在线预测，获取文本的相似程度。 - -* [图像分割-基于deeplabv3p_xception65_humanseg](../../demo/serving/module_serving/semantic_segmentation_deeplabv3p_xception65_humanseg) - -  该示例展示了利用deeplabv3p_xception65_humanseg完成图像分割服务化部署和在线预测，获取识别结果和分割后的图像。 - -* [中文情感分析-基于senta_lstm](../../demo/serving/module_serving/sentiment_analysis_senta_lstm) - -  该示例展示了利用senta_lstm完成中文文本情感分析服务化部署和在线预测，获取文本的情感分析结果。 - -## 客户端请求新版模型的方式 -对某些新版模型，客户端请求方式有所变化，更接近本地预测的请求方式，以降低学习成本。 -以lac(2.1.0)为例，使用上述方法进行请求将提示： -```python -{ - "Warnning": "This usage is out of date, please use 'application/json' as content-type to post to /predict/lac. See 'https://github.com/PaddlePaddle/PaddleHub/blob/release/v1.6/docs/tutorial/serving.md' for more details." -} -``` -对于lac(2.1.0)，请求的方式如下： -```python -# coding: utf8 -import requests -import json - -if __name__ == "__main__": - # 指定用于预测的文本并生成字典{"text": [text_1, text_2, ... ]} - text = ["今天是个好日子", "天气预报说今天要下雨"] - # 以key的方式指定text传入预测方法的时的参数，此例中为"data" - # 对应本地部署，则为lac.analysis_lexical(texts=[text1, text2]) - data = {"texts": text, "batch_size": 2} - # 指定预测方法为lac并发送post请求 - url = "http://127.0.0.1:8866/predict/lac" - # 指定post请求的headers为application/json方式 - headers = {"Content-Type": "application/json"} - - r = requests.post(url=url, headers=headers, data=json.dumps(data)) - - # 打印预测结果 - print(json.dumps(r.json(), indent=4, ensure_ascii=False)) -``` +* [人脸检测-基于pyramidbox_lite_server_mask](../../demo/serving/module_serving/object_detection_pyramidbox_lite_server_mask) -此Demo的具体信息和代码请参见[LAC Serving_2.1.0](../../demo/serving/module_serving/lexical_analysis_lac/lac_2.1.0_serving_demo.py)。 +  该示例展示了利用pyramidbox_lite_server_mask完成人脸口罩检测，检测人脸位置以及戴口枣的置信度。 ## Bert Service 除了预训练模型一键服务部署功能之外，PaddleHub Serving还具有`Bert Service`功能，支持ernie_tiny、bert等模型快速部署，对外提供可靠的在线embedding服务，具体信息请参见[Bert Service](./bert_service.md)。 -- GitLab