diff --git a/modules/text/text_generation/ernie_tiny/README.md b/modules/text/text_generation/ernie_tiny/README.md deleted file mode 100644 index 15c6543286655543a7e1345d3b1fdf7394c6b8ef..0000000000000000000000000000000000000000 --- a/modules/text/text_generation/ernie_tiny/README.md +++ /dev/null @@ -1,126 +0,0 @@ -# ernie_tiny - -|模型名称|ernie_tiny| -| :--- | :---: | -|类别|图像 - 图像生成| -|网络|SPADEGenerator| -|数据集|coco_stuff| -|是否支持Fine-tuning|否| -|模型大小|74MB| -|最新更新日期|2021-12-14| -|数据指标|-| - - -## 一、模型基本信息 - -- ### 应用效果展示 - - 样例结果示例： -

- -
- -- ### 模型介绍 - - - 本模块采用一个像素风格迁移网络 Pix2PixHD，能够根据输入的语义分割标签生成照片风格的图片。为了解决模型归一化层导致标签语义信息丢失的问题，向 Pix2PixHD 的生成器网络中添加了 SPADE（Spatially-Adaptive - Normalization）空间自适应归一化模块，通过两个卷积层保留了归一化时训练的缩放与偏置参数的空间维度，以增强生成图片的质量。语义风格标签图像可以参考[coco_stuff数据集](https://github.com/nightrome/cocostuff)获取, 也可以通过[PaddleGAN repo中的该项目](https://github.com/PaddlePaddle/PaddleGAN/blob/87537ad9d4eeda17eaa5916c6a585534ab989ea8/docs/zh_CN/tutorials/photopen.md)来自定义生成图像进行体验。 - - - -## 二、安装 - -- ### 1、环境依赖 - - ppgan - -- ### 2、安装 - - - ```shell - $ hub install photopen - ``` - - 如您安装时遇到问题，可参考：[零基础windows安装](../../../../docs/docs_ch/get_start/windows_quickstart.md) - | [零基础Linux安装](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [零基础MacOS安装](../../../../docs/docs_ch/get_start/mac_quickstart.md) - -## 三、模型API预测 - -- ### 1、命令行预测 - - - ```shell - # Read from a file - $ hub run photopen --input_path "/PATH/TO/IMAGE" - ``` - - 通过命令行方式实现图像生成模型的调用，更多请见 [PaddleHub命令行指令](../../../../docs/docs_ch/tutorial/cmd_usage.rst) - -- ### 2、预测代码示例 - - - ```python - import paddlehub as hub - - module = hub.Module(name="photopen") - input_path = ["/PATH/TO/IMAGE"] - # Read from a file - module.photo_transfer(paths=input_path, output_dir='./transfer_result/', use_gpu=True) - ``` - -- ### 3、API - - - ```python - photo_transfer(images=None, paths=None, output_dir='./transfer_result/', use_gpu=False, visualization=True): - ``` - - 图像转换生成API。 - - - **参数** - - - images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\]；
- - paths (list\[str\]): 图片的路径；
- - output\_dir (str): 结果保存的路径；
- - use\_gpu (bool): 是否使用 GPU；
- - visualization(bool): 是否保存结果到本地文件夹 - - -## 四、服务部署 - -- PaddleHub Serving可以部署一个在线图像转换生成服务。 - -- ### 第一步：启动PaddleHub Serving - - - 运行启动命令： - - ```shell - $ hub serving start -m photopen - ``` - - - 这样就完成了一个图像转换生成的在线服务API的部署，默认端口号为8866。 - - - **NOTE:** 如使用GPU预测，则需要在启动服务之前，请设置CUDA\_VISIBLE\_DEVICES环境变量，否则不用设置。 - -- ### 第二步：发送预测请求 - - - 配置好服务端，以下数行代码即可实现发送预测请求，获取预测结果 - - - ```python - import requests - import json - import cv2 - import base64 - - - def cv2_to_base64(image): - data = cv2.imencode('.jpg', image)[1] - return base64.b64encode(data.tostring()).decode('utf8') - - # 发送HTTP请求 - data = {'images':[cv2_to_base64(cv2.imread("/PATH/TO/IMAGE"))]} - headers = {"Content-type": "application/json"} - url = "http://127.0.0.1:8866/predict/photopen" - r = requests.post(url=url, headers=headers, data=json.dumps(data)) - - # 打印预测结果 - print(r.json()["results"]) - -## 五、更新历史 - -* 1.0.0 - - 初始发布 - - - ```shell - $ hub install ernie_tiny==1.1.0 - ``` diff --git a/modules/text/text_generation/ernie_tiny/README_en.md b/modules/text/text_generation/ernie_tiny/README_en.md deleted file mode 100644 index 373348799089cc370f90335e6290c0ce38a8a11c..0000000000000000000000000000000000000000 --- a/modules/text/text_generation/ernie_tiny/README_en.md +++ /dev/null @@ -1,171 +0,0 @@ -# ernie_tiny - -|Module Name|ernie_tiny| -| :--- | :---: | -|Category|object detection| -|Network|faster_rcnn| -|Dataset|COCO2017| -|Fine-tuning supported or not|No| -|Module Size|161MB| -|Latest update date|2021-03-15| -|Data indicators|-| - - -## I.Basic Information - -- ### Application Effect Display - - Sample results： -

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- -- ### Module Introduction - - - Faster_RCNN is a two-stage detector, it consists of feature extraction, proposal, classification and refinement processes. This module is trained on COCO2017 dataset, and can be used for object detection. - - -## II.Installation - -- ### 1、Environmental Dependence - - - paddlepaddle >= 1.6.2 - - - paddlehub >= 1.6.0 | [How to install PaddleHub](../../../../docs/docs_en/get_start/installation.rst) - -- ### 2、Installation - - - ```shell - $ hub install faster_rcnn_resnet50_fpn_coco2017 - ``` - - In case of any problems during installation, please refer to: [Windows_Quickstart](../../../../docs/docs_en/get_start/windows_quickstart.md) | [Linux_Quickstart](../../../../docs/docs_en/get_start/linux_quickstart.md) | [Mac_Quickstart](../../../../docs/docs_en/get_start/mac_quickstart.md) - -## III.Module API Prediction - -- ### 1、Command line Prediction - - - ```shell - $ hub run faster_rcnn_resnet50_fpn_coco2017 --input_path "/PATH/TO/IMAGE" - ``` - - If you want to call the Hub module through the command line, please refer to: [PaddleHub Command Line Instruction](../../../../docs/docs_ch/tutorial/cmd_usage.rst) - -- ### 2、Prediction Code Example - - - ```python - import paddlehub as hub - import cv2 - - object_detector = hub.Module(name="faster_rcnn_resnet50_fpn_coco2017") - result = object_detector.object_detection(images=[cv2.imread('/PATH/TO/IMAGE')]) - # or - # result = object_detector.object_detection((paths=['/PATH/TO/IMAGE']) - ``` - -- ### 3、API - - - ```python - def object_detection(paths=None, - images=None, - batch_size=1, - use_gpu=False, - output_dir='detection_result', - score_thresh=0.5, - visualization=True) - ``` - - - Detection API, detect positions of all objects in image - - - **Parameters** - - - paths (list[str]): image path; - - images (list\[numpy.ndarray\]): image data, ndarray.shape is in the format [H, W, C], BGR; - - batch_size (int): the size of batch; - - use_gpu (bool): use GPU or not; **set the CUDA_VISIBLE_DEVICES environment variable first if you are using GPU** - - output_dir (str): save path of images; - - score\_thresh (float): confidence threshold；
- - visualization (bool): Whether to save the results as picture files; - - **NOTE:** choose one parameter to provide data from paths and images - - - **Return** - - - res (list\[dict\]): results - - data (list): detection results, each element in the list is dict - - confidence (float): the confidence of the result - - label (str): label - - left (int): the upper left corner x coordinate of the detection box - - top (int): the upper left corner y coordinate of the detection box - - right (int): the lower right corner x coordinate of the detection box - - bottom (int): the lower right corner y coordinate of the detection box - - save\_path (str, optional): output path for saving results - - - - ```python - def save_inference_model(dirname, - model_filename=None, - params_filename=None, - combined=True) - ``` - - Save model to specific path - - - **Parameters** - - - dirname: output dir for saving model - - model\_filename: filename for saving model - - params\_filename: filename for saving parameters - - combined: whether save parameters into one file - - -## IV.Server Deployment - -- PaddleHub Serving can deploy an online service of object detection. - -- ### Step 1: Start PaddleHub Serving - - - Run the startup command： - - ```shell - $ hub serving start -m faster_rcnn_resnet50_fpn_coco2017 - ``` - - - The servitization API is now deployed and the default port number is 8866. - - - **NOTE:** If GPU is used for prediction, set CUDA_VISIBLE_DEVICES environment variable before the service, otherwise it need not be set. - -- ### Step 2: Send a predictive request - - - With a configured server, use the following lines of code to send the prediction request and obtain the result - - - ```python - import requests - import json - import cv2 - import base64 - - - def cv2_to_base64(image): - data = cv2.imencode('.jpg', image)[1] - return base64.b64encode(data.tostring()).decode('utf8') - - # Send an HTTP request - data = {'images':[cv2_to_base64(cv2.imread("/PATH/TO/IMAGE"))]} - headers = {"Content-type": "application/json"} - url = "http://127.0.0.1:8866/predict/faster_rcnn_resnet50_fpn_coco2017" - r = requests.post(url=url, headers=headers, data=json.dumps(data)) - - # print prediction results - print(r.json()["results"]) - ``` - - -## V.Release Note - -* 1.0.0 - - First release - -* 1.0.1 - - Fix the problem of reading numpy - - ```shell - $ hub install ernie_tiny==1.1.0 - ```