dyning
GitHub
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# 模型服务化部署
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[Paddle Serving](https://github.com/PaddlePaddle/Serving) 旨在帮助深度学习开发者轻易部署在线预测服务，支持一键部署工业级的服务能力、客户端和服务端之间高并发和高效通信、并支持多种编程语言开发客户端等特点，详细使用请参考 [Paddle Serving 相关文档](https://github.com/PaddlePaddle/Serving)。
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## 一、简介
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[Paddle Serving](https://github.com/PaddlePaddle/Serving) 旨在帮助深度学习开发者轻易部署在线预测服务，支持一键部署工业级的服务能力、客户端和服务端之间高并发和高效通信、并支持多种编程语言开发客户端。
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该部分以HTTP预测服务部署为例，介绍怎样在PaddleClas中使用PaddleServing部署模型服务。
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## 二、Serving安装
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Serving官网推荐使用docker安装并部署Serving环境。首先需要拉取docker环境并创建基于Serving的docker。
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```shell
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nvidia-docker pull hub.baidubce.com/paddlepaddle/serving:0.2.0-gpu
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nvidia-docker run -p 9292:9292 --name test -dit hub.baidubce.com/paddlepaddle/serving:0.2.0-gpu
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nvidia-docker exec -it test bash
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```
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进入docker后，需要安装Serving相关的python包。
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```shell
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pip install paddlepaddle-gpu
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pip install paddle-serving-client
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pip install paddle-serving-server-gpu
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```
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* 如果安装速度太慢，可以通过`-i https://pypi.tuna.tsinghua.edu.cn/simple`更换源，加速安装过程。
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* 如果希望部署CPU服务，可以安装serving-server的cpu版本，安装命令如下。
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```shell
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pip install paddle-serving-server
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```
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### 三、导出模型
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使用`tools/export_serving_model.py`脚本导出Serving模型，以`ResNet50_vd`为例，使用方法如下。
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```shell
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python tools/export_serving_model.py -m ResNet50_vd -p ./pretrained/ResNet50_vd_pretrained/ -o serving
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```
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最终在serving文件夹下会生成`ppcls_client_conf`与`ppcls_model`两个文件夹，分别存储了client配置、模型参数与结构文件。
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### 四、服务部署与请求
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* 使用下面的方式启动Serving服务。
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```shell
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python tools/serving/image_service_gpu.py serving/ppcls_model workdir 9292
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```
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其中`serving/ppcls_model`为刚才保存的Serving模型地址，`workdir`为为工作目录，`9292`为服务的端口号。
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* 使用下面的脚本向Serving服务发送识别请求，并返回结果。
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```
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python tools/serving/image_http_client.py 9292 ./docs/images/logo.png
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```
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`9292`为发送请求的端口号，需要与服务启动时的端口号保持一致，`./docs/images/logo.png`为待识别的图像文件。最终返回Top1识别结果的类别ID以及概率值。
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* 更多的服务部署类型，如`RPC预测服务`等，可以参考Serving的github官网：[https://github.com/PaddlePaddle/Serving/tree/develop/python/examples/imagenet](https://github.com/PaddlePaddle/Serving/tree/develop/python/examples/imagenet)
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import os
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from ppcls.modeling import architectures
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import paddle.fluid as fluid
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import paddle_serving_client.io as serving_io
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument("-m", "--model", type=str)
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parser.add_argument("-p", "--pretrained_model", type=str)
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parser.add_argument("-o", "--output_path", type=str, default="")
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parser.add_argument("--class_dim", type=int, default=1000)
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parser.add_argument("--img_size", type=int, default=224)
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return parser.parse_args()
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def create_input(img_size=224):
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image = fluid.data(
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name='image', shape=[None, 3, img_size, img_size], dtype='float32')
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return image
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def create_model(args, model, input, class_dim=1000):
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if args.model == "GoogLeNet":
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out, _, _ = model.net(input=input, class_dim=class_dim)
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else:
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out = model.net(input=input, class_dim=class_dim)
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out = fluid.layers.softmax(out)
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return out
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def main():
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args = parse_args()
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model = architectures.__dict__[args.model]()
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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startup_prog = fluid.Program()
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infer_prog = fluid.Program()
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with fluid.program_guard(infer_prog, startup_prog):
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with fluid.unique_name.guard():
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image = create_input(args.img_size)
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out = create_model(args, model, image, class_dim=args.class_dim)
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infer_prog = infer_prog.clone(for_test=True)
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fluid.load(
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program=infer_prog, model_path=args.pretrained_model, executor=exe)
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model_path = os.path.join(args.output_path, "ppcls_model")
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conf_path = os.path.join(args.output_path, "ppcls_client_conf")
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serving_io.save_model(model_path, conf_path, {"image": image},
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{"prediction": out}, infer_prog)
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if __name__ == "__main__":
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main()
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import requests
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import base64
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import json
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import sys
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import numpy as np
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py_version = sys.version_info[0]
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def predict(image_path, server):
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if py_version == 2:
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image = base64.b64encode(open(image_path).read())
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else:
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image = base64.b64encode(open(image_path, "rb").read()).decode("utf-8")
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req = json.dumps({"feed": [{"image": image}], "fetch": ["prediction"]})
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r = requests.post(
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server, data=req, headers={"Content-Type": "application/json"})
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try:
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pred = r.json()["result"]["prediction"][0]
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cls_id = np.argmax(pred)
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score = pred[cls_id]
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pred = {"cls_id": cls_id, "score": score}
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return pred
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except ValueError:
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print(r.text)
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return r
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if __name__ == "__main__":
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server = "http://127.0.0.1:{}/image/prediction".format(sys.argv[1])
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image_file = sys.argv[2]
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res = predict(image_file, server)
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print("res:", res)
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import sys
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import base64
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from paddle_serving_server.web_service import WebService
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import utils
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class ImageService(WebService):
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def __init__(self, name):
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super(ImageService, self).__init__(name=name)
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self.operators = self.create_operators()
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def create_operators(self):
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size = 224
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img_mean = [0.485, 0.456, 0.406]
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img_std = [0.229, 0.224, 0.225]
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img_scale = 1.0 / 255.0
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decode_op = utils.DecodeImage()
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resize_op = utils.ResizeImage(resize_short=256)
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crop_op = utils.CropImage(size=(size, size))
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normalize_op = utils.NormalizeImage(
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scale=img_scale, mean=img_mean, std=img_std)
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totensor_op = utils.ToTensor()
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return [decode_op, resize_op, crop_op, normalize_op, totensor_op]
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def _process_image(self, data, ops):
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for op in ops:
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data = op(data)
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return data
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def preprocess(self, feed={}, fetch=[]):
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feed_batch = []
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for ins in feed:
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if "image" not in ins:
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raise ("feed data error!")
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sample = base64.b64decode(ins["image"])
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img = self._process_image(sample, self.operators)
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feed_batch.append({"image": img})
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return feed_batch, fetch
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image_service = ImageService(name="image")
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image_service.load_model_config(sys.argv[1])
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image_service.prepare_server(
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workdir=sys.argv[2], port=int(sys.argv[3]), device="cpu")
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image_service.run_server()
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image_service.run_flask()
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import sys
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import base64
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from paddle_serving_server_gpu.web_service import WebService
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import utils
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class ImageService(WebService):
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def __init__(self, name):
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super(ImageService, self).__init__(name=name)
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self.operators = self.create_operators()
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def create_operators(self):
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size = 224
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img_mean = [0.485, 0.456, 0.406]
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img_std = [0.229, 0.224, 0.225]
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img_scale = 1.0 / 255.0
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decode_op = utils.DecodeImage()
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resize_op = utils.ResizeImage(resize_short=256)
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crop_op = utils.CropImage(size=(size, size))
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normalize_op = utils.NormalizeImage(
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scale=img_scale, mean=img_mean, std=img_std)
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totensor_op = utils.ToTensor()
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return [decode_op, resize_op, crop_op, normalize_op, totensor_op]
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def _process_image(self, data, ops):
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for op in ops:
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data = op(data)
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return data
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def preprocess(self, feed={}, fetch=[]):
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feed_batch = []
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for ins in feed:
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if "image" not in ins:
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raise ("feed data error!")
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sample = base64.b64decode(ins["image"])
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img = self._process_image(sample, self.operators)
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feed_batch.append({"image": img})
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return feed_batch, fetch
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image_service = ImageService(name="image")
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image_service.load_model_config(sys.argv[1])
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image_service.set_gpus("0")
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image_service.prepare_server(
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workdir=sys.argv[2], port=int(sys.argv[3]), device="gpu")
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image_service.run_server()
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image_service.run_flask()
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import cv2
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import numpy as np
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class DecodeImage(object):
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def __init__(self, to_rgb=True):
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self.to_rgb = to_rgb
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def __call__(self, img):
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data = np.frombuffer(img, dtype='uint8')
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img = cv2.imdecode(data, 1)
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if self.to_rgb:
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assert img.shape[2] == 3, 'invalid shape of image[%s]' % (
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img.shape)
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img = img[:, :, ::-1]
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return img
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class ResizeImage(object):
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def __init__(self, resize_short=None):
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self.resize_short = resize_short
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def __call__(self, img):
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img_h, img_w = img.shape[:2]
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percent = float(self.resize_short) / min(img_w, img_h)
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w = int(round(img_w * percent))
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h = int(round(img_h * percent))
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return cv2.resize(img, (w, h))
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class CropImage(object):
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def __init__(self, size):
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if type(size) is int:
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self.size = (size, size)
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else:
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self.size = size
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def __call__(self, img):
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w, h = self.size
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img_h, img_w = img.shape[:2]
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w_start = (img_w - w) // 2
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h_start = (img_h - h) // 2
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w_end = w_start + w
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h_end = h_start + h
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return img[h_start:h_end, w_start:w_end, :]
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class NormalizeImage(object):
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def __init__(self, scale=None, mean=None, std=None):
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self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
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mean = mean if mean is not None else [0.485, 0.456, 0.406]
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std = std if std is not None else [0.229, 0.224, 0.225]
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shape = (1, 1, 3)
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self.mean = np.array(mean).reshape(shape).astype('float32')
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self.std = np.array(std).reshape(shape).astype('float32')
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def __call__(self, img):
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return (img.astype('float32') * self.scale - self.mean) / self.std
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class ToTensor(object):
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def __init__(self):
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pass
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def __call__(self, img):
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img = img.transpose((2, 0, 1))
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return img
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