Merge remote-tracking branch 'upstream/develop' into 0.2.2-dev

sync

README.md

@@ -264,8 +264,8 @@ curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"url": "https://pa
 
 ### About Efficiency
 - [How to profile Paddle Serving latency?](python/examples/util)
-- [How to optimize performance?(Chinese)](doc/MULTI_SERVICE_ON_ONE_GPU_CN.md)
-- [Deploy multi-services on one GPU(Chinese)](doc/PERFORMANCE_OPTIM_CN.md)
+- [How to optimize performance?(Chinese)](doc/PERFORMANCE_OPTIM_CN.md)
+- [Deploy multi-services on one GPU(Chinese)](doc/MULTI_SERVICE_ON_ONE_GPU_CN.md)
 - [CPU Benchmarks(Chinese)](doc/BENCHMARKING.md)
 - [GPU Benchmarks(Chinese)](doc/GPU_BENCHMARKING.md)
 

README_CN.md

@@ -270,8 +270,8 @@ curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"url": "https://pa
 
 ### 关于Paddle Serving性能
 - [如何测试Paddle Serving性能？](python/examples/util/)
-- [如何优化性能?](doc/MULTI_SERVICE_ON_ONE_GPU_CN.md)
-- [在一张GPU上启动多个预测服务](doc/PERFORMANCE_OPTIM_CN.md)
+- [如何优化性能?](doc/PERFORMANCE_OPTIM_CN.md)
+- [在一张GPU上启动多个预测服务](doc/MULTI_SERVICE_ON_ONE_GPU_CN.md)
 - [CPU版Benchmarks](doc/BENCHMARKING.md)
 - [GPU版Benchmarks](doc/GPU_BENCHMARKING.md)
 

core/cube/cube-agent/src/agent/util.go

@@ -83,9 +83,6 @@ func JsonReq(method, requrl string, timeout int, kv *map[string]string,
 }
 
 func GetHdfsMeta(src string) (master, ugi, path string, err error) {
-	//src = "hdfs://root:rootpasst@st1-inf-platform0.st01.baidu.com:54310/user/mis_user/news_dnn_ctr_cube_1/1501836820/news_dnn_ctr_cube_1_part54.tar"
-	//src = "hdfs://st1-inf-platform0.st01.baidu.com:54310/user/mis_user/news_dnn_ctr_cube_1/1501836820/news_dnn_ctr_cube_1_part54.tar"
-
 	ugiBegin := strings.Index(src, "//")
 	ugiPos := strings.LastIndex(src, "@")
 	if ugiPos != -1 && ugiBegin != -1 {

core/general-client/include/general_model.h

@@ -69,9 +69,15 @@ class ModelRes {
   const std::vector<int64_t>& get_int64_by_name(const std::string& name) {
     return _int64_value_map[name];
   }
+  std::vector<int64_t>&& get_int64_by_name_with_rv(const std::string& name) {
+    return std::move(_int64_value_map[name]);
+  }
   const std::vector<float>& get_float_by_name(const std::string& name) {
     return _float_value_map[name];
   }
+  std::vector<float>&& get_float_by_name_with_rv(const std::string& name) {
+    return std::move(_float_value_map[name]);
+  }
   const std::vector<int>& get_shape(const std::string& name) {
     return _shape_map[name];
   }
@@ -121,10 +127,18 @@ class PredictorRes {
                                                 const std::string& name) {
     return _models[model_idx].get_int64_by_name(name);
   }
+  std::vector<int64_t>&& get_int64_by_name_with_rv(const int model_idx,
+                                                   const std::string& name) {
+    return std::move(_models[model_idx].get_int64_by_name_with_rv(name));
+  }
   const std::vector<float>& get_float_by_name(const int model_idx,
                                               const std::string& name) {
     return _models[model_idx].get_float_by_name(name);
   }
+  std::vector<float>&& get_float_by_name_with_rv(const int model_idx,
+                                                 const std::string& name) {
+    return std::move(_models[model_idx].get_float_by_name_with_rv(name));
+  }
   const std::vector<int>& get_shape(const int model_idx,
                                     const std::string& name) {
     return _models[model_idx].get_shape(name);

core/general-client/src/general_model.cpp

@@ -258,9 +258,10 @@ int PredictorClient::batch_predict(
       ModelRes model;
       model.set_engine_name(output.engine_name());
 
+      int idx = 0;
+
       for (auto &name : fetch_name) {
         // int idx = _fetch_name_to_idx[name];
-        int idx = 0;
         int shape_size = output.insts(0).tensor_array(idx).shape_size();
         VLOG(2) << "fetch var " << name << " index " << idx << " shape size "
                 << shape_size;
@@ -279,9 +280,9 @@ int PredictorClient::batch_predict(
         idx += 1;
       }
 
+      idx = 0;
       for (auto &name : fetch_name) {
         // int idx = _fetch_name_to_idx[name];
-        int idx = 0;
         if (_fetch_name_to_type[name] == 0) {
           VLOG(2) << "ferch var " << name << "type int";
           model._int64_value_map[name].resize(
@@ -536,9 +537,9 @@ int PredictorClient::numpy_predict(
       ModelRes model;
       model.set_engine_name(output.engine_name());
 
+      int idx = 0;
       for (auto &name : fetch_name) {
         // int idx = _fetch_name_to_idx[name];
-        int idx = 0;
         int shape_size = output.insts(0).tensor_array(idx).shape_size();
         VLOG(2) << "fetch var " << name << " index " << idx << " shape size "
                 << shape_size;
@@ -557,9 +558,10 @@ int PredictorClient::numpy_predict(
         idx += 1;
       }
 
+      idx = 0;
+
       for (auto &name : fetch_name) {
         // int idx = _fetch_name_to_idx[name];
-        int idx = 0;
         if (_fetch_name_to_type[name] == 0) {
           VLOG(2) << "ferch var " << name << "type int";
           model._int64_value_map[name].resize(

core/general-client/src/pybind_general_model.cpp

@@ -32,14 +32,23 @@ PYBIND11_MODULE(serving_client, m) {
       .def(py::init())
       .def("get_int64_by_name",
            [](PredictorRes &self, int model_idx, std::string &name) {
-             return self.get_int64_by_name(model_idx, name);
-           },
-           py::return_value_policy::reference)
+             // see more: https://github.com/pybind/pybind11/issues/1042
+             std::vector<int64_t> *ptr = new std::vector<int64_t>(
+                 std::move(self.get_int64_by_name_with_rv(model_idx, name)));
+             auto capsule = py::capsule(ptr, [](void *p) {
+               delete reinterpret_cast<std::vector<int64_t> *>(p);
+             });
+             return py::array(ptr->size(), ptr->data(), capsule);
+           })
       .def("get_float_by_name",
            [](PredictorRes &self, int model_idx, std::string &name) {
-             return self.get_float_by_name(model_idx, name);
-           },
-           py::return_value_policy::reference)
+             std::vector<float> *ptr = new std::vector<float>(
+                 std::move(self.get_float_by_name_with_rv(model_idx, name)));
+             auto capsule = py::capsule(ptr, [](void *p) {
+               delete reinterpret_cast<std::vector<float> *>(p);
+             });
+             return py::array(ptr->size(), ptr->data(), capsule);
+           })
       .def("get_shape",
            [](PredictorRes &self, int model_idx, std::string &name) {
              return self.get_shape(model_idx, name);

doc/BERT_10_MINS_CN.md

@@ -13,10 +13,10 @@ import paddlehub as hub
 model_name = "bert_chinese_L-12_H-768_A-12"
 module = hub.Module(model_name)
 inputs, outputs, program = module.context(trainable=True, max_seq_len=20)
-feed_keys = ["input_ids", "position_ids", "segment_ids", "input_mask", "pooled_output", "sequence_output"]
+feed_keys = ["input_ids", "position_ids", "segment_ids", "input_mask"]
 fetch_keys = ["pooled_output", "sequence_output"]
 feed_dict = dict(zip(feed_keys, [inputs[x] for x in feed_keys]))
-fetch_dict = dict(zip(fetch_keys, [outputs[x]] for x in fetch_keys))
+fetch_dict = dict(zip(fetch_keys, [outputs[x] for x in fetch_keys]))
 
 import paddle_serving_client.io as serving_io
 serving_io.save_model("bert_seq20_model", "bert_seq20_client", feed_dict, fetch_dict, program)

doc/SAVE.md

@@ -10,8 +10,9 @@ serving_io.save_model("imdb_model", "imdb_client_conf",
                       {"words": data}, {"prediction": prediction},
                       fluid.default_main_program())
 ```
-`imdb_model` is the server side model with serving configurations. `imdb_client_conf` is the client rpc configurations. Serving has a 
-dictionary for `Feed` and `Fetch` variables for client to assign. In the example, `{"words": data}` is the feed dict that specify the input of saved inference model. `{"prediction": prediction}` is the fetch dic that specify the output of saved inference model. An alias name can be defined for feed and fetch variables. An example of how to use alias name
+`imdb_model` is the server side model with serving configurations. `imdb_client_conf` is the client rpc configurations. 
+
+Serving has a dictionary for `Feed` and `Fetch` variables for client to assign. In the example, `{"words": data}` is the feed dict that specify the input of saved inference model. `{"prediction": prediction}` is the fetch dic that specify the output of saved inference model. An alias name can be defined for feed and fetch variables. An example of how to use alias name
  is as follows:
  ``` python
  from paddle_serving_client import Client
@@ -35,10 +36,14 @@ for line in sys.stdin:
 If you have saved model files using Paddle's `save_inference_model` API, you can use Paddle Serving's` inference_model_to_serving` API to convert it into a model file that can be used for Paddle Serving.
 ```
 import paddle_serving_client.io as serving_io
-serving_io.inference_model_to_serving(dirname, model_filename=None, params_filename=None, serving_server="serving_server", serving_client="serving_client")
+serving_io.inference_model_to_serving(dirname, serving_server="serving_server", serving_client="serving_client", model_filename=None, params_filename=None )
 ```
 dirname (str) - Path of saved model files. Program file and parameter files are saved in this directory.
-model_filename (str, optional) - The name of file to load the inference program. If it is None, the default filename __model__ will be used. Default: None.
-paras_filename (str, optional) - The name of file to load all parameters. It is only used for the case that all parameters were saved in a single binary file. If parameters were saved in separate files, set it as None. Default: None.
+
 serving_server (str, optional) - The path of model files and configuration files for server. Default: "serving_server".
+
 serving_client (str, optional) - The path of configuration files for client. Default: "serving_client".
+
+model_filename (str, optional) - The name of file to load the inference program. If it is None, the default filename `__model__` will be used. Default: None.
+
+paras_filename (str, optional) - The name of file to load all parameters. It is only used for the case that all parameters were saved in a single binary file. If parameters were saved in separate files, set it as None. Default: None.

doc/SAVE_CN.md

@@ -11,7 +11,9 @@ serving_io.save_model("imdb_model", "imdb_client_conf",
                       {"words": data}, {"prediction": prediction},
                       fluid.default_main_program())
 ```
-imdb_model是具有服务配置的服务器端模型。 imdb_client_conf是客户端rpc配置。 Serving有一个 提供给用户存放Feed和Fetch变量信息的字典。 在示例中，`{words”：data}` 是用于指定已保存推理模型输入的提要字典。`{"prediction"：projection}`是指定保存的推理模型输出的字典。可以为feed和fetch变量定义一个别名。 如何使用别名的例子 示例如下：
+imdb_model是具有服务配置的服务器端模型。 imdb_client_conf是客户端rpc配置。
+
+Serving有一个提供给用户存放Feed和Fetch变量信息的字典。 在示例中，`{"words"：data}` 是用于指定已保存推理模型输入的提要字典。`{"prediction"：projection}`是指定保存的推理模型输出的字典。可以为feed和fetch变量定义一个别名。 如何使用别名的例子 示例如下：
 
  ``` python
  from paddle_serving_client import Client
@@ -35,10 +37,14 @@ for line in sys.stdin:
 如果已使用Paddle 的`save_inference_model`接口保存出预测要使用的模型，则可以通过Paddle Serving的`inference_model_to_serving`接口转换成可用于Paddle Serving的模型文件。
 ```
 import paddle_serving_client.io as serving_io
-serving_io.inference_model_to_serving(dirname, model_filename=None, params_filename=None, serving_server="serving_server", serving_client="serving_client")
+serving_io.inference_model_to_serving(dirname, serving_server="serving_server", serving_client="serving_client",  model_filename=None, params_filename=None)
 ```
 dirname (str) – 需要转换的模型文件存储路径，Program结构文件和参数文件均保存在此目录。
-model_filename (str，可选) – 存储需要转换的模型Inference Program结构的文件名称。如果设置为None，则使用 __model__ 作为默认的文件名。默认值为None。
+
+serving_server (str, 可选) - 转换后的模型文件和配置文件的存储路径。默认值为serving_server。
+
+serving_client (str, 可选) - 转换后的客户端配置文件存储路径。默认值为serving_client。
+
+model_filename (str，可选) – 存储需要转换的模型Inference Program结构的文件名称。如果设置为None，则使用 `__model__` 作为默认的文件名。默认值为None。
+
 params_filename (str，可选) – 存储需要转换的模型所有参数的文件名称。当且仅当所有模型参数被保存在一个单独的二进制文件中，它才需要被指定。如果模型参数是存储在各自分离的文件中，设置它的值为None。默认值为None。
-serving_server (str, 可选) - 转换后的模型文件和配置文件的存储路径。默认值为"serving_server"。
-serving_client (str, 可选) - 转换后的客户端配置文件存储路径。默认值为"serving_client"。

doc/UWSGI_DEPLOY.md

-# 使用uwsgi启动HTTP预测服务
+# Deploy HTTP service with uWSGI
 
-在提供的fit_a_line示例中，启动HTTP预测服务后会看到有以下信息：
+([简体中文](./UWSGI_DEPLOY_CN.md)|English)
+
+In fit_a_line example, after starting the HTTP prediction service, you will see the following information:
 
 ```shell
 web service address:
@@ -13,46 +15,31 @@ http://10.127.3.150:9393/uci/prediction
  * Running on http://0.0.0.0:9393/ (Press CTRL+C to quit)
 ```
 
-这里会提示启动的HTTP服务是开发模式，并不能用于生产环境的部署。Flask启动的服务环境不够稳定也无法承受大量请求的并发，实际部署过程中配合需要WSGI（Web Server Gateway Interface）使用。
+Here you will be prompted that the HTTP service started is in development mode and cannot be used for production deployment. 
+The prediction service started by Flask is not stable enough to withstand the concurrency of a large number of requests. In the actual deployment process, WSGI (Web Server Gateway Interface) is used.
 
-下面我们展示一下如何使用[uWSGI](https://github.com/unbit/uwsgi)模块来部署HTTP预测服务用于生产环境。
+Next, we will show how to use the [uWSGI] (https://github.com/unbit/uwsgi) module to deploy HTTP prediction services for production environments.
 
-编写HTTP服务脚本
 
 ```python
 #uwsgi_service.py
 from paddle_serving_server.web_service import WebService
-from flask import Flask, request
 
-#配置预测服务
+#Define prediction service
 uci_service = WebService(name = "uci")
 uci_service.load_model_config("./uci_housing_model")
 uci_service.prepare_server(workdir="./workdir", port=int(9500), device="cpu")
 uci_service.run_server()
-
-#配置flask服务
-app_instance = Flask(__name__)
-@app_instance.before_first_request
-def init():
-    global uci_service
-    uci_service._launch_web_service()
-
-service_name = "/" + uci_service.name + "/prediction"
-@app_instance.route(service_name, methods=["POST"])
-def run():
-    return uci_service.get_prediction(request)
-
-#run方法用于直接调试中直接启动服务
-if __name__ == "__main__":
-    app_instance.run()
+#Get flask application
+app_instance = uci_service.get_app_instance()
 ```
 
-使用uwsgi启动HTTP服务
+Start service with uWSGI
 
 ```bash
-uwsgi --http :9000 --wsgi-file uwsgi_service.py --callable app_instance --processes 4
+uwsgi --http :9393 --module uwsgi_service:app_instance
 ```
 
-使用--processes参数可以指定服务的进程数，请注意目前Serving HTTP 服务暂时不支持多线程的方式使用。
+Use the --processes parameter to specify the number of service processes. 
 
-更多uWSGI的信息请参考[uWSGI使用文档](https://uwsgi-docs.readthedocs.io/en/latest/)
+For more information about uWSGI, please refer to [uWSGI documentation](https://uwsgi-docs.readthedocs.io/en/latest/)

doc/UWSGI_DEPLOY_CN.md

+# 使用uwsgi启动HTTP预测服务
+
+(简体中文|[English](./UWSGI_DEPLOY.md))
+
+在提供的fit_a_line示例中，启动HTTP预测服务后会看到有以下信息：
+
+```shell
+web service address:
+http://10.127.3.150:9393/uci/prediction
+ * Serving Flask app "serve" (lazy loading)
+ * Environment: production
+   WARNING: This is a development server. Do not use it in a production deployment.
+   Use a production WSGI server instead.
+ * Debug mode: off
+ * Running on http://0.0.0.0:9393/ (Press CTRL+C to quit)
+```
+
+这里会提示启动的HTTP服务是开发模式，并不能用于生产环境的部署。Flask启动的服务环境不够稳定也无法承受大量请求的并发，实际部署过程中配合需要WSGI（Web Server Gateway Interface）使用。
+
+下面我们展示一下如何使用[uWSGI](https://github.com/unbit/uwsgi)模块来部署HTTP预测服务用于生产环境。
+
+编写HTTP服务脚本
+
+```python
+#uwsgi_service.py
+from paddle_serving_server.web_service import WebService
+
+#配置预测服务
+uci_service = WebService(name = "uci")
+uci_service.load_model_config("./uci_housing_model")
+uci_service.prepare_server(workdir="./workdir", port=int(9500), device="cpu")
+uci_service.run_server()
+#获取flask服务
+app_instance = uci_service.get_app_instance()
+```
+
+使用uwsgi启动HTTP服务
+
+```bash
+uwsgi --http :9393 --module uwsgi_service:app_instance
+```
+
+使用--processes参数可以指定服务的进程数。
+
+更多uWSGI的信息请参考[uWSGI使用文档](https://uwsgi-docs.readthedocs.io/en/latest/)

python/examples/cascade_rcnn/README.md

+# Cascade RCNN model on Paddle Serving
+
+([简体中文](./README_CN.md)|English)
+
+### Get The Cascade RCNN Model
+```
+sh get_data.sh
+```
+If you want to have more detection models, please refer to [Paddle Detection Model Zoo](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.2/docs/MODEL_ZOO_cn.md)
+
+### Start the service
+```
+python -m paddle_serving_server_gpu.serve --model serving_server --port 9292 --gpu_id 0
+```
+
+### Perform prediction
+```
+python test_client.py 
+```
+
+Image with bounding boxes and json result would be saved in `output` folder.

python/examples/cascade_rcnn/README_CN.md

+# 使用Paddle Serving部署Cascade RCNN模型
+
+(简体中文|[English](./README.md))
+
+## 获得Cascade RCNN模型
+```
+sh get_data.sh
+```
+如果你想要更多的检测模型，请参考[Paddle检测模型库](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.2/docs/MODEL_ZOO_cn.md)
+
+### 启动服务
+```
+python -m paddle_serving_server_gpu.serve --model serving_server --port 9292 --gpu_id 0
+```
+
+### 执行预测
+```
+python test_client.py
+```
+
+客户端已经为图片做好了后处理，在`output`文件夹下存放各个框的json格式信息还有后处理结果图片。

python/examples/cascade_rcnn/get_data.sh

+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/cascade_rcnn_r50_fpx_1x_serving.tar.gz
+tar xf cascade_rcnn_r50_fpx_1x_serving.tar.gz

python/examples/imagenet/README.md

@@ -15,34 +15,35 @@ sh get_model.sh
 pip install paddle_serving_app
 ```
 
-### HTTP Infer
+### HTTP Service
 
 launch server side
 ```
-python image_classification_service.py ResNet50_vd_model workdir 9393 #cpu inference service
+python resnet50_web_service.py ResNet50_vd_model cpu 9696 #cpu inference service
 ```
 ```
-python image_classification_service_gpu.py ResNet50_vd_model workdir 9393 #gpu inference service
+python resnet50_web_service.py ResNet50_vd_model gpu 9696 #gpu inference service
 ```
 
 
 client send inference request
 ```
-python image_http_client.py
+curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"image": "https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg"}], "fetch": ["score"]}' http://127.0.0.1:9696/image/prediction
 ```
-### RPC Infer
+
+### RPC Service
 
 launch server side
 ```
-python -m paddle_serving_server.serve --model ResNet50_vd_model --port 9393 #cpu inference service
+python -m paddle_serving_server.serve --model ResNet50_vd_model --port 9696 #cpu inference service
 ```
 
 ```
-python -m paddle_serving_server_gpu.serve --model ResNet50_vd_model --port 9393 --gpu_ids 0 #gpu inference service
+python -m paddle_serving_server_gpu.serve --model ResNet50_vd_model --port 9696 --gpu_ids 0 #gpu inference service
 ```
 
 client send inference request
 ```
 python image_rpc_client.py ResNet50_vd_client_config/serving_client_conf.prototxt
 ```
-*the port of server side in this example is 9393, the sample data used by client side is in the folder ./data. These parameter can be modified in practice*
+*the port of server side in this example is 9696

python/examples/imagenet/README_CN.md

@@ -15,34 +15,35 @@ sh get_model.sh
 pip install paddle_serving_app
 ```
 
-### 执行HTTP预测服务
+### HTTP服务
 
 启动server端
 ```
-python image_classification_service.py ResNet50_vd_model workdir 9393 #cpu预测服务
+python image_classification_service.py ResNet50_vd_model cpu 9696 #cpu预测服务
 ```
 ```
-python image_classification_service_gpu.py ResNet50_vd_model workdir 9393 #gpu预测服务
+python image_classification_service.py ResNet50_vd_model gpu 9696 #gpu预测服务
 ```
 
 
-client端进行预测
+发送HTTP POST请求
 ```
-python image_http_client.py
+curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"image": "https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg"}], "fetch": ["score"]}' http://127.0.0.1:9696/image/prediction
 ```
-### 执行RPC预测服务
+
+### RPC服务
 
 启动server端
 ```
-python -m paddle_serving_server.serve --model ResNet50_vd_model --port 9393 #cpu预测服务
+python -m paddle_serving_server.serve --model ResNet50_vd_model --port 9696 #cpu预测服务
 ```
 
 ```
-python -m paddle_serving_server_gpu.serve --model ResNet50_vd_model --port 9393 --gpu_ids 0 #gpu预测服务
+python -m paddle_serving_server_gpu.serve --model ResNet50_vd_model --port 9696 --gpu_ids 0 #gpu预测服务
 ```
 
 client端进行预测
 ```
 python image_rpc_client.py ResNet50_vd_client_config/serving_client_conf.prototxt
 ```
-*server端示例中服务端口为9393端口，client端示例中数据来自./data文件夹，server端地址为本地9393端口，可根据实际情况更改脚本。*
+*server端示例中服务端口为9696端口

python/examples/imagenet/benchmark.py

@@ -39,8 +39,8 @@ def single_func(idx, resource):
         client.connect([resource["endpoint"][idx % len(resource["endpoint"])]])
 
         start = time.time()
-        for i in range(1000):
-            img = reader.process_image(img_list[i]).reshape(-1)
+        for i in range(100):
+            img = reader.process_image(img_list[i])
             fetch_map = client.predict(feed={"image": img}, fetch=["score"])
         end = time.time()
         return [[end - start]]
@@ -49,7 +49,7 @@ def single_func(idx, resource):
 
 if __name__ == "__main__":
     multi_thread_runner = MultiThreadRunner()
-    endpoint_list = ["127.0.0.1:9393"]
+    endpoint_list = ["127.0.0.1:9292"]
     #card_num = 4
     #for i in range(args.thread):
     #    endpoint_list.append("127.0.0.1:{}".format(9295 + i % card_num))

python/examples/imagenet/benchmark_batch.py

@@ -24,6 +24,7 @@ from paddle_serving_client.utils import MultiThreadRunner
 from paddle_serving_client.utils import benchmark_args
 import requests
 import json
+import base64
 from image_reader import ImageReader
 
 args = benchmark_args()
@@ -36,6 +37,10 @@ def single_func(idx, resource):
     img_list = []
     for i in range(1000):
         img_list.append(open("./image_data/n01440764/" + file_list[i]).read())
+    profile_flags = False
+    if "FLAGS_profile_client" in os.environ and os.environ[
+            "FLAGS_profile_client"]:
+        profile_flags = True
     if args.request == "rpc":
         reader = ImageReader()
         fetch = ["score"]
@@ -46,23 +51,43 @@ def single_func(idx, resource):
         for i in range(1000):
             if args.batch_size >= 1:
                 feed_batch = []
+                i_start = time.time()
                 for bi in range(args.batch_size):
                     img = reader.process_image(img_list[i])
-                    img = img.reshape(-1)
                     feed_batch.append({"image": img})
+                i_end = time.time()
+                if profile_flags:
+                    print("PROFILE\tpid:{}\timage_pre_0:{} image_pre_1:{}".
+                          format(os.getpid(),
+                                 int(round(i_start * 1000000)),
+                                 int(round(i_end * 1000000))))
+
                 result = client.predict(feed=feed_batch, fetch=fetch)
             else:
                 print("unsupport batch size {}".format(args.batch_size))
 
     elif args.request == "http":
-        raise ("no batch predict for http")
+        py_version = 2
+        server = "http://" + resource["endpoint"][idx % len(resource[
+            "endpoint"])] + "/image/prediction"
+        start = time.time()
+        for i in range(1000):
+            if py_version == 2:
+                image = base64.b64encode(
+                    open("./image_data/n01440764/" + file_list[i]).read())
+            else:
+                image = base64.b64encode(open(image_path, "rb").read()).decode(
+                    "utf-8")
+            req = json.dumps({"feed": [{"image": image}], "fetch": ["score"]})
+            r = requests.post(
+                server, data=req, headers={"Content-Type": "application/json"})
     end = time.time()
     return [[end - start]]
 
 
 if __name__ == '__main__':
     multi_thread_runner = MultiThreadRunner()
-    endpoint_list = ["127.0.0.1:9393"]
+    endpoint_list = ["127.0.0.1:9292"]
     #endpoint_list = endpoint_list + endpoint_list + endpoint_list
     result = multi_thread_runner.run(single_func, args.thread,
                                      {"endpoint": endpoint_list})

python/examples/imagenet/image_classification_service_gpu.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import sys
-import cv2
-import base64
-import numpy as np
-from paddle_serving_app import ImageReader
-from paddle_serving_server_gpu.web_service import WebService
-
-
-class ImageService(WebService):
-    def preprocess(self, feed={}, fetch=[]):
-        reader = ImageReader()
-        feed_batch = []
-        for ins in feed:
-            if "image" not in ins:
-                raise ("feed data error!")
-            sample = base64.b64decode(ins["image"])
-            img = reader.process_image(sample)
-            feed_batch.append({"image": img})
-        return feed_batch, fetch
-
-
-image_service = ImageService(name="image")
-image_service.load_model_config(sys.argv[1])
-image_service.set_gpus("0,1")
-image_service.prepare_server(
-    workdir=sys.argv[2], port=int(sys.argv[3]), device="gpu")
-image_service.run_server()
-image_service.run_flask()

python/examples/imagenet/image_http_client.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import requests
-import base64
-import json
-import time
-import os
-import sys
-
-py_version = sys.version_info[0]
-
-
-def predict(image_path, server):
-    if py_version == 2:
-        image = base64.b64encode(open(image_path).read())
-    else:
-        image = base64.b64encode(open(image_path, "rb").read()).decode("utf-8")
-    req = json.dumps({"feed": [{"image": image}], "fetch": ["score"]})
-    r = requests.post(
-        server, data=req, headers={"Content-Type": "application/json"})
-    try:
-        print(r.json()["result"]["score"])
-    except ValueError:
-        print(r.text)
-    return r
-
-
-if __name__ == "__main__":
-    server = "http://127.0.0.1:9393/image/prediction"
-    image_list = os.listdir("./image_data/n01440764/")
-    start = time.time()
-    for img in image_list:
-        image_file = "./image_data/n01440764/" + img
-        res = predict(image_file, server)
-    end = time.time()
-    print(end - start)

python/examples/imagenet/image_reader.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import cv2
-import numpy as np
-
-
-class ImageReader():
-    def __init__(self):
-        self.image_mean = [0.485, 0.456, 0.406]
-        self.image_std = [0.229, 0.224, 0.225]
-        self.image_shape = [3, 224, 224]
-        self.resize_short_size = 256
-        self.interpolation = None
-
-    def resize_short(self, img, target_size, interpolation=None):
-        """resize image
-
-        Args:
-            img: image data
-            target_size: resize short target size
-            interpolation: interpolation mode
-
-        Returns:
-            resized image data
-        """
-        percent = float(target_size) / min(img.shape[0], img.shape[1])
-        resized_width = int(round(img.shape[1] * percent))
-        resized_height = int(round(img.shape[0] * percent))
-        if interpolation:
-            resized = cv2.resize(
-                img, (resized_width, resized_height),
-                interpolation=interpolation)
-        else:
-            resized = cv2.resize(img, (resized_width, resized_height))
-        return resized
-
-    def crop_image(self, img, target_size, center):
-        """crop image
-
-        Args:
-            img: images data
-            target_size: crop target size
-            center: crop mode
-
-        Returns:
-            img: cropped image data
-        """
-        height, width = img.shape[:2]
-        size = target_size
-        if center == True:
-            w_start = (width - size) // 2
-            h_start = (height - size) // 2
-        else:
-            w_start = np.random.randint(0, width - size + 1)
-            h_start = np.random.randint(0, height - size + 1)
-        w_end = w_start + size
-        h_end = h_start + size
-        img = img[h_start:h_end, w_start:w_end, :]
-        return img
-
-    def process_image(self, sample):
-        """ process_image """
-        mean = self.image_mean
-        std = self.image_std
-        crop_size = self.image_shape[1]
-
-        data = np.fromstring(sample, np.uint8)
-        img = cv2.imdecode(data, cv2.IMREAD_COLOR)
-
-        if img is None:
-            print("img is None, pass it.")
-            return None
-
-        if crop_size > 0:
-            target_size = self.resize_short_size
-            img = self.resize_short(
-                img, target_size, interpolation=self.interpolation)
-            img = self.crop_image(img, target_size=crop_size, center=True)
-
-        img = img[:, :, ::-1]
-
-        img = img.astype('float32').transpose((2, 0, 1)) / 255
-        img_mean = np.array(mean).reshape((3, 1, 1))
-        img_std = np.array(std).reshape((3, 1, 1))
-        img -= img_mean
-        img /= img_std
-        return img

python/examples/imagenet/imagenet.label

+tench, Tinca tinca,
+goldfish, Carassius auratus,
+great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias,
+tiger shark, Galeocerdo cuvieri,
+hammerhead, hammerhead shark,
+electric ray, crampfish, numbfish, torpedo,
+stingray,
+cock,
+hen,
+ostrich, Struthio camelus,
+brambling, Fringilla montifringilla,
+goldfinch, Carduelis carduelis,
+house finch, linnet, Carpodacus mexicanus,
+junco, snowbird,
+indigo bunting, indigo finch, indigo bird, Passerina cyanea,
+robin, American robin, Turdus migratorius,
+bulbul,
+jay,
+magpie,
+chickadee,
+water ouzel, dipper,
+kite,
+bald eagle, American eagle, Haliaeetus leucocephalus,
+vulture,
+great grey owl, great gray owl, Strix nebulosa,
+European fire salamander, Salamandra salamandra,
+common newt, Triturus vulgaris,
+eft,
+spotted salamander, Ambystoma maculatum,
+axolotl, mud puppy, Ambystoma mexicanum,
+bullfrog, Rana catesbeiana,
+tree frog, tree-frog,
+tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui,
+loggerhead, loggerhead turtle, Caretta caretta,
+leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea,
+mud turtle,
+terrapin,
+box turtle, box tortoise,
+banded gecko,
+common iguana, iguana, Iguana iguana,
+American chameleon, anole, Anolis carolinensis,
+whiptail, whiptail lizard,
+agama,
+frilled lizard, Chlamydosaurus kingi,
+alligator lizard,
+Gila monster, Heloderma suspectum,
+green lizard, Lacerta viridis,
+African chameleon, Chamaeleo chamaeleon,
+Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis,
+African crocodile, Nile crocodile, Crocodylus niloticus,
+American alligator, Alligator mississipiensis,
+triceratops,
+thunder snake, worm snake, Carphophis amoenus,
+ringneck snake, ring-necked snake, ring snake,
+hognose snake, puff adder, sand viper,
+green snake, grass snake,
+king snake, kingsnake,
+garter snake, grass snake,
+water snake,
+vine snake,
+night snake, Hypsiglena torquata,
+boa constrictor, Constrictor constrictor,
+rock python, rock snake, Python sebae,
+Indian cobra, Naja naja,
+green mamba,
+sea snake,
+horned viper, cerastes, sand viper, horned asp, Cerastes cornutus,
+diamondback, diamondback rattlesnake, Crotalus adamanteus,
+sidewinder, horned rattlesnake, Crotalus cerastes,
+trilobite,
+harvestman, daddy longlegs, Phalangium opilio,
+scorpion,
+black and gold garden spider, Argiope aurantia,
+barn spider, Araneus cavaticus,
+garden spider, Aranea diademata,
+black widow, Latrodectus mactans,
+tarantula,
+wolf spider, hunting spider,
+tick,
+centipede,
+black grouse,
+ptarmigan,
+ruffed grouse, partridge, Bonasa umbellus,
+prairie chicken, prairie grouse, prairie fowl,
+peacock,
+quail,
+partridge,
+African grey, African gray, Psittacus erithacus,
+macaw,
+sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita,
+lorikeet,
+coucal,
+bee eater,
+hornbill,
+hummingbird,
+jacamar,
+toucan,
+drake,
+red-breasted merganser, Mergus serrator,
+goose,
+black swan, Cygnus atratus,
+tusker,
+echidna, spiny anteater, anteater,
+platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus,
+wallaby, brush kangaroo,
+koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus,
+wombat,
+jellyfish,
+sea anemone, anemone,
+brain coral,
+flatworm, platyhelminth,
+nematode, nematode worm, roundworm,
+conch,
+snail,
+slug,
+sea slug, nudibranch,
+chiton, coat-of-mail shell, sea cradle, polyplacophore,
+chambered nautilus, pearly nautilus, nautilus,
+Dungeness crab, Cancer magister,
+rock crab, Cancer irroratus,
+fiddler crab,
+king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica,
+American lobster, Northern lobster, Maine lobster, Homarus americanus,
+spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish,
+crayfish, crawfish, crawdad, crawdaddy,
+hermit crab,
+isopod,
+white stork, Ciconia ciconia,
+black stork, Ciconia nigra,
+spoonbill,
+flamingo,
+little blue heron, Egretta caerulea,
+American egret, great white heron, Egretta albus,
+bittern,
+crane,
+limpkin, Aramus pictus,
+European gallinule, Porphyrio porphyrio,
+American coot, marsh hen, mud hen, water hen, Fulica americana,
+bustard,
+ruddy turnstone, Arenaria interpres,
+red-backed sandpiper, dunlin, Erolia alpina,
+redshank, Tringa totanus,
+dowitcher,
+oystercatcher, oyster catcher,
+pelican,
+king penguin, Aptenodytes patagonica,
+albatross, mollymawk,
+grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus,
+killer whale, killer, orca, grampus, sea wolf, Orcinus orca,
+dugong, Dugong dugon,
+sea lion,
+Chihuahua,
+Japanese spaniel,
+Maltese dog, Maltese terrier, Maltese,
+Pekinese, Pekingese, Peke,
+Shih-Tzu,
+Blenheim spaniel,
+papillon,
+toy terrier,
+Rhodesian ridgeback,
+Afghan hound, Afghan,
+basset, basset hound,
+beagle,
+bloodhound, sleuthhound,
+bluetick,
+black-and-tan coonhound,
+Walker hound, Walker foxhound,
+English foxhound,
+redbone,
+borzoi, Russian wolfhound,
+Irish wolfhound,
+Italian greyhound,
+whippet,
+Ibizan hound, Ibizan Podenco,
+Norwegian elkhound, elkhound,
+otterhound, otter hound,
+Saluki, gazelle hound,
+Scottish deerhound, deerhound,
+Weimaraner,
+Staffordshire bullterrier, Staffordshire bull terrier,
+American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier,
+Bedlington terrier,
+Border terrier,
+Kerry blue terrier,
+Irish terrier,
+Norfolk terrier,
+Norwich terrier,
+Yorkshire terrier,
+wire-haired fox terrier,
+Lakeland terrier,
+Sealyham terrier, Sealyham,
+Airedale, Airedale terrier,
+cairn, cairn terrier,
+Australian terrier,
+Dandie Dinmont, Dandie Dinmont terrier,
+Boston bull, Boston terrier,
+miniature schnauzer,
+giant schnauzer,
+standard schnauzer,
+Scotch terrier, Scottish terrier, Scottie,
+Tibetan terrier, chrysanthemum dog,
+silky terrier, Sydney silky,
+soft-coated wheaten terrier,
+West Highland white terrier,
+Lhasa, Lhasa apso,
+flat-coated retriever,
+curly-coated retriever,
+golden retriever,
+Labrador retriever,
+Chesapeake Bay retriever,
+German short-haired pointer,
+vizsla, Hungarian pointer,
+English setter,
+Irish setter, red setter,
+Gordon setter,
+Brittany spaniel,
+clumber, clumber spaniel,
+English springer, English springer spaniel,
+Welsh springer spaniel,
+cocker spaniel, English cocker spaniel, cocker,
+Sussex spaniel,
+Irish water spaniel,
+kuvasz,
+schipperke,
+groenendael,
+malinois,
+briard,
+kelpie,
+komondor,
+Old English sheepdog, bobtail,
+Shetland sheepdog, Shetland sheep dog, Shetland,
+collie,
+Border collie,
+Bouvier des Flandres, Bouviers des Flandres,
+Rottweiler,
+German shepherd, German shepherd dog, German police dog, alsatian,
+Doberman, Doberman pinscher,
+miniature pinscher,
+Greater Swiss Mountain dog,
+Bernese mountain dog,
+Appenzeller,
+EntleBucher,
+boxer,
+bull mastiff,
+Tibetan mastiff,
+French bulldog,
+Great Dane,
+Saint Bernard, St Bernard,
+Eskimo dog, husky,
+malamute, malemute, Alaskan malamute,
+Siberian husky,
+dalmatian, coach dog, carriage dog,
+affenpinscher, monkey pinscher, monkey dog,
+basenji,
+pug, pug-dog,
+Leonberg,
+Newfoundland, Newfoundland dog,
+Great Pyrenees,
+Samoyed, Samoyede,
+Pomeranian,
+chow, chow chow,
+keeshond,
+Brabancon griffon,
+Pembroke, Pembroke Welsh corgi,
+Cardigan, Cardigan Welsh corgi,
+toy poodle,
+miniature poodle,
+standard poodle,
+Mexican hairless,
+timber wolf, grey wolf, gray wolf, Canis lupus,
+white wolf, Arctic wolf, Canis lupus tundrarum,
+red wolf, maned wolf, Canis rufus, Canis niger,
+coyote, prairie wolf, brush wolf, Canis latrans,
+dingo, warrigal, warragal, Canis dingo,
+dhole, Cuon alpinus,
+African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus,
+hyena, hyaena,
+red fox, Vulpes vulpes,
+kit fox, Vulpes macrotis,
+Arctic fox, white fox, Alopex lagopus,
+grey fox, gray fox, Urocyon cinereoargenteus,
+tabby, tabby cat,
+tiger cat,
+Persian cat,
+Siamese cat, Siamese,
+Egyptian cat,
+cougar, puma, catamount, mountain lion, painter, panther, Felis concolor,
+lynx, catamount,
+leopard, Panthera pardus,
+snow leopard, ounce, Panthera uncia,
+jaguar, panther, Panthera onca, Felis onca,
+lion, king of beasts, Panthera leo,
+tiger, Panthera tigris,
+cheetah, chetah, Acinonyx jubatus,
+brown bear, bruin, Ursus arctos,
+American black bear, black bear, Ursus americanus, Euarctos americanus,
+ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus,
+sloth bear, Melursus ursinus, Ursus ursinus,
+mongoose,
+meerkat, mierkat,
+tiger beetle,
+ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle,
+ground beetle, carabid beetle,
+long-horned beetle, longicorn, longicorn beetle,
+leaf beetle, chrysomelid,
+dung beetle,
+rhinoceros beetle,
+weevil,
+fly,
+bee,
+ant, emmet, pismire,
+grasshopper, hopper,
+cricket,
+walking stick, walkingstick, stick insect,
+cockroach, roach,
+mantis, mantid,
+cicada, cicala,
+leafhopper,
+lacewing, lacewing fly,
+"dragonfly, darning needle, devils darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
+damselfly,
+admiral,
+ringlet, ringlet butterfly,
+monarch, monarch butterfly, milkweed butterfly, Danaus plexippus,
+cabbage butterfly,
+sulphur butterfly, sulfur butterfly,
+lycaenid, lycaenid butterfly,
+starfish, sea star,
+sea urchin,
+sea cucumber, holothurian,
+wood rabbit, cottontail, cottontail rabbit,
+hare,
+Angora, Angora rabbit,
+hamster,
+porcupine, hedgehog,
+fox squirrel, eastern fox squirrel, Sciurus niger,
+marmot,
+beaver,
+guinea pig, Cavia cobaya,
+sorrel,
+zebra,
+hog, pig, grunter, squealer, Sus scrofa,
+wild boar, boar, Sus scrofa,
+warthog,
+hippopotamus, hippo, river horse, Hippopotamus amphibius,
+ox,
+water buffalo, water ox, Asiatic buffalo, Bubalus bubalis,
+bison,
+ram, tup,
+bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis,
+ibex, Capra ibex,
+hartebeest,
+impala, Aepyceros melampus,
+gazelle,
+Arabian camel, dromedary, Camelus dromedarius,
+llama,
+weasel,
+mink,
+polecat, fitch, foulmart, foumart, Mustela putorius,
+black-footed ferret, ferret, Mustela nigripes,
+otter,
+skunk, polecat, wood pussy,
+badger,
+armadillo,
+three-toed sloth, ai, Bradypus tridactylus,
+orangutan, orang, orangutang, Pongo pygmaeus,
+gorilla, Gorilla gorilla,
+chimpanzee, chimp, Pan troglodytes,
+gibbon, Hylobates lar,
+siamang, Hylobates syndactylus, Symphalangus syndactylus,
+guenon, guenon monkey,
+patas, hussar monkey, Erythrocebus patas,
+baboon,
+macaque,
+langur,
+colobus, colobus monkey,
+proboscis monkey, Nasalis larvatus,
+marmoset,
+capuchin, ringtail, Cebus capucinus,
+howler monkey, howler,
+titi, titi monkey,
+spider monkey, Ateles geoffroyi,
+squirrel monkey, Saimiri sciureus,
+Madagascar cat, ring-tailed lemur, Lemur catta,
+indri, indris, Indri indri, Indri brevicaudatus,
+Indian elephant, Elephas maximus,
+African elephant, Loxodonta africana,
+lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens,
+giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca,
+barracouta, snoek,
+eel,
+coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch,
+rock beauty, Holocanthus tricolor,
+anemone fish,
+sturgeon,
+gar, garfish, garpike, billfish, Lepisosteus osseus,
+lionfish,
+puffer, pufferfish, blowfish, globefish,
+abacus,
+abaya,
+"academic gown, academic robe, judges robe",
+accordion, piano accordion, squeeze box,
+acoustic guitar,
+aircraft carrier, carrier, flattop, attack aircraft carrier,
+airliner,
+airship, dirigible,
+altar,
+ambulance,
+amphibian, amphibious vehicle,
+analog clock,
+apiary, bee house,
+apron,
+ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin,
+assault rifle, assault gun,
+backpack, back pack, knapsack, packsack, rucksack, haversack,
+bakery, bakeshop, bakehouse,
+balance beam, beam,
+balloon,
+ballpoint, ballpoint pen, ballpen, Biro,
+Band Aid,
+banjo,
+bannister, banister, balustrade, balusters, handrail,
+barbell,
+barber chair,
+barbershop,
+barn,
+barometer,
+barrel, cask,
+barrow, garden cart, lawn cart, wheelbarrow,
+baseball,
+basketball,
+bassinet,
+bassoon,
+bathing cap, swimming cap,
+bath towel,
+bathtub, bathing tub, bath, tub,
+beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon,
+beacon, lighthouse, beacon light, pharos,
+beaker,
+bearskin, busby, shako,
+beer bottle,
+beer glass,
+bell cote, bell cot,
+bib,
+bicycle-built-for-two, tandem bicycle, tandem,
+bikini, two-piece,
+binder, ring-binder,
+binoculars, field glasses, opera glasses,
+birdhouse,
+boathouse,
+bobsled, bobsleigh, bob,
+bolo tie, bolo, bola tie, bola,
+bonnet, poke bonnet,
+bookcase,
+bookshop, bookstore, bookstall,
+bottlecap,
+bow,
+bow tie, bow-tie, bowtie,
+brass, memorial tablet, plaque,
+brassiere, bra, bandeau,
+breakwater, groin, groyne, mole, bulwark, seawall, jetty,
+breastplate, aegis, egis,
+broom,
+bucket, pail,
+buckle,
+bulletproof vest,
+bullet train, bullet,
+butcher shop, meat market,
+cab, hack, taxi, taxicab,
+caldron, cauldron,
+candle, taper, wax light,
+cannon,
+canoe,
+can opener, tin opener,
+cardigan,
+car mirror,
+carousel, carrousel, merry-go-round, roundabout, whirligig,
+"carpenters kit, tool kit",
+carton,
+car wheel,
+cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM,
+cassette,
+cassette player,
+castle,
+catamaran,
+CD player,
+cello, violoncello,
+cellular telephone, cellular phone, cellphone, cell, mobile phone,
+chain,
+chainlink fence,
+chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour,
+chain saw, chainsaw,
+chest,
+chiffonier, commode,
+chime, bell, gong,
+china cabinet, china closet,
+Christmas stocking,
+church, church building,
+cinema, movie theater, movie theatre, movie house, picture palace,
+cleaver, meat cleaver, chopper,
+cliff dwelling,
+cloak,
+clog, geta, patten, sabot,
+cocktail shaker,
+coffee mug,
+coffeepot,
+coil, spiral, volute, whorl, helix,
+combination lock,
+computer keyboard, keypad,
+confectionery, confectionary, candy store,
+container ship, containership, container vessel,
+convertible,
+corkscrew, bottle screw,
+cornet, horn, trumpet, trump,
+cowboy boot,
+cowboy hat, ten-gallon hat,
+cradle,
+crane,
+crash helmet,
+crate,
+crib, cot,
+Crock Pot,
+croquet ball,
+crutch,
+cuirass,
+dam, dike, dyke,
+desk,
+desktop computer,
+dial telephone, dial phone,
+diaper, nappy, napkin,
+digital clock,
+digital watch,
+dining table, board,
+dishrag, dishcloth,
+dishwasher, dish washer, dishwashing machine,
+disk brake, disc brake,
+dock, dockage, docking facility,
+dogsled, dog sled, dog sleigh,
+dome,
+doormat, welcome mat,
+drilling platform, offshore rig,
+drum, membranophone, tympan,
+drumstick,
+dumbbell,
+Dutch oven,
+electric fan, blower,
+electric guitar,
+electric locomotive,
+entertainment center,
+envelope,
+espresso maker,
+face powder,
+feather boa, boa,
+file, file cabinet, filing cabinet,
+fireboat,
+fire engine, fire truck,
+fire screen, fireguard,
+flagpole, flagstaff,
+flute, transverse flute,
+folding chair,
+football helmet,
+forklift,
+fountain,
+fountain pen,
+four-poster,
+freight car,
+French horn, horn,
+frying pan, frypan, skillet,
+fur coat,
+garbage truck, dustcart,
+gasmask, respirator, gas helmet,
+gas pump, gasoline pump, petrol pump, island dispenser,
+goblet,
+go-kart,
+golf ball,
+golfcart, golf cart,
+gondola,
+gong, tam-tam,
+gown,
+grand piano, grand,
+greenhouse, nursery, glasshouse,
+grille, radiator grille,
+grocery store, grocery, food market, market,
+guillotine,
+hair slide,
+hair spray,
+half track,
+hammer,
+hamper,
+hand blower, blow dryer, blow drier, hair dryer, hair drier,
+hand-held computer, hand-held microcomputer,
+handkerchief, hankie, hanky, hankey,
+hard disc, hard disk, fixed disk,
+harmonica, mouth organ, harp, mouth harp,
+harp,
+harvester, reaper,
+hatchet,
+holster,
+home theater, home theatre,
+honeycomb,
+hook, claw,
+hoopskirt, crinoline,
+horizontal bar, high bar,
+horse cart, horse-cart,
+hourglass,
+iPod,
+iron, smoothing iron,
+"jack-o-lantern",
+jean, blue jean, denim,
+jeep, landrover,
+jersey, T-shirt, tee shirt,
+jigsaw puzzle,
+jinrikisha, ricksha, rickshaw,
+joystick,
+kimono,
+knee pad,
+knot,
+lab coat, laboratory coat,
+ladle,
+lampshade, lamp shade,
+laptop, laptop computer,
+lawn mower, mower,
+lens cap, lens cover,
+letter opener, paper knife, paperknife,
+library,
+lifeboat,
+lighter, light, igniter, ignitor,
+limousine, limo,
+liner, ocean liner,
+lipstick, lip rouge,
+Loafer,
+lotion,
+loudspeaker, speaker, speaker unit, loudspeaker system, speaker system,
+"loupe, jewelers loupe",
+lumbermill, sawmill,
+magnetic compass,
+mailbag, postbag,
+mailbox, letter box,
+maillot,
+maillot, tank suit,
+manhole cover,
+maraca,
+marimba, xylophone,
+mask,
+matchstick,
+maypole,
+maze, labyrinth,
+measuring cup,
+medicine chest, medicine cabinet,
+megalith, megalithic structure,
+microphone, mike,
+microwave, microwave oven,
+military uniform,
+milk can,
+minibus,
+miniskirt, mini,
+minivan,
+missile,
+mitten,
+mixing bowl,
+mobile home, manufactured home,
+Model T,
+modem,
+monastery,
+monitor,
+moped,
+mortar,
+mortarboard,
+mosque,
+mosquito net,
+motor scooter, scooter,
+mountain bike, all-terrain bike, off-roader,
+mountain tent,
+mouse, computer mouse,
+mousetrap,
+moving van,
+muzzle,
+nail,
+neck brace,
+necklace,
+nipple,
+notebook, notebook computer,
+obelisk,
+oboe, hautboy, hautbois,
+ocarina, sweet potato,
+odometer, hodometer, mileometer, milometer,
+oil filter,
+organ, pipe organ,
+oscilloscope, scope, cathode-ray oscilloscope, CRO,
+overskirt,
+oxcart,
+oxygen mask,
+packet,
+paddle, boat paddle,
+paddlewheel, paddle wheel,
+padlock,
+paintbrush,
+"pajama, pyjama, pjs, jammies",
+palace,
+panpipe, pandean pipe, syrinx,
+paper towel,
+parachute, chute,
+parallel bars, bars,
+park bench,
+parking meter,
+passenger car, coach, carriage,
+patio, terrace,
+pay-phone, pay-station,
+pedestal, plinth, footstall,
+pencil box, pencil case,
+pencil sharpener,
+perfume, essence,
+Petri dish,
+photocopier,
+pick, plectrum, plectron,
+pickelhaube,
+picket fence, paling,
+pickup, pickup truck,
+pier,
+piggy bank, penny bank,
+pill bottle,
+pillow,
+ping-pong ball,
+pinwheel,
+pirate, pirate ship,
+pitcher, ewer,
+"plane, carpenters plane, woodworking plane",
+planetarium,
+plastic bag,
+plate rack,
+plow, plough,
+"plunger, plumbers helper",
+Polaroid camera, Polaroid Land camera,
+pole,
+police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria,
+poncho,
+pool table, billiard table, snooker table,
+pop bottle, soda bottle,
+pot, flowerpot,
+"potters wheel",
+power drill,
+prayer rug, prayer mat,
+printer,
+prison, prison house,
+projectile, missile,
+projector,
+puck, hockey puck,
+punching bag, punch bag, punching ball, punchball,
+purse,
+quill, quill pen,
+quilt, comforter, comfort, puff,
+racer, race car, racing car,
+racket, racquet,
+radiator,
+radio, wireless,
+radio telescope, radio reflector,
+rain barrel,
+recreational vehicle, RV, R.V.,
+reel,
+reflex camera,
+refrigerator, icebox,
+remote control, remote,
+restaurant, eating house, eating place, eatery,
+revolver, six-gun, six-shooter,
+rifle,
+rocking chair, rocker,
+rotisserie,
+rubber eraser, rubber, pencil eraser,
+rugby ball,
+rule, ruler,
+running shoe,
+safe,
+safety pin,
+saltshaker, salt shaker,
+sandal,
+sarong,
+sax, saxophone,
+scabbard,
+scale, weighing machine,
+school bus,
+schooner,
+scoreboard,
+screen, CRT screen,
+screw,
+screwdriver,
+seat belt, seatbelt,
+sewing machine,
+shield, buckler,
+shoe shop, shoe-shop, shoe store,
+shoji,
+shopping basket,
+shopping cart,
+shovel,
+shower cap,
+shower curtain,
+ski,
+ski mask,
+sleeping bag,
+slide rule, slipstick,
+sliding door,
+slot, one-armed bandit,
+snorkel,
+snowmobile,
+snowplow, snowplough,
+soap dispenser,
+soccer ball,
+sock,
+solar dish, solar collector, solar furnace,
+sombrero,
+soup bowl,
+space bar,
+space heater,
+space shuttle,
+spatula,
+speedboat,
+"spider web, spiders web",
+spindle,
+sports car, sport car,
+spotlight, spot,
+stage,
+steam locomotive,
+steel arch bridge,
+steel drum,
+stethoscope,
+stole,
+stone wall,
+stopwatch, stop watch,
+stove,
+strainer,
+streetcar, tram, tramcar, trolley, trolley car,
+stretcher,
+studio couch, day bed,
+stupa, tope,
+submarine, pigboat, sub, U-boat,
+suit, suit of clothes,
+sundial,
+sunglass,
+sunglasses, dark glasses, shades,
+sunscreen, sunblock, sun blocker,
+suspension bridge,
+swab, swob, mop,
+sweatshirt,
+swimming trunks, bathing trunks,
+swing,
+switch, electric switch, electrical switch,
+syringe,
+table lamp,
+tank, army tank, armored combat vehicle, armoured combat vehicle,
+tape player,
+teapot,
+teddy, teddy bear,
+television, television system,
+tennis ball,
+thatch, thatched roof,
+theater curtain, theatre curtain,
+thimble,
+thresher, thrasher, threshing machine,
+throne,
+tile roof,
+toaster,
+tobacco shop, tobacconist shop, tobacconist,
+toilet seat,
+torch,
+totem pole,
+tow truck, tow car, wrecker,
+toyshop,
+tractor,
+trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi,
+tray,
+trench coat,
+tricycle, trike, velocipede,
+trimaran,
+tripod,
+triumphal arch,
+trolleybus, trolley coach, trackless trolley,
+trombone,
+tub, vat,
+turnstile,
+typewriter keyboard,
+umbrella,
+unicycle, monocycle,
+upright, upright piano,
+vacuum, vacuum cleaner,
+vase,
+vault,
+velvet,
+vending machine,
+vestment,
+viaduct,
+violin, fiddle,
+volleyball,
+waffle iron,
+wall clock,
+wallet, billfold, notecase, pocketbook,
+wardrobe, closet, press,
+warplane, military plane,
+washbasin, handbasin, washbowl, lavabo, wash-hand basin,
+washer, automatic washer, washing machine,
+water bottle,
+water jug,
+water tower,
+whiskey jug,
+whistle,
+wig,
+window screen,
+window shade,
+Windsor tie,
+wine bottle,
+wing,
+wok,
+wooden spoon,
+wool, woolen, woollen,
+worm fence, snake fence, snake-rail fence, Virginia fence,
+wreck,
+yawl,
+yurt,
+web site, website, internet site, site,
+comic book,
+crossword puzzle, crossword,
+street sign,
+traffic light, traffic signal, stoplight,
+book jacket, dust cover, dust jacket, dust wrapper,
+menu,
+plate,
+guacamole,
+consomme,
+hot pot, hotpot,
+trifle,
+ice cream, icecream,
+ice lolly, lolly, lollipop, popsicle,
+French loaf,
+bagel, beigel,
+pretzel,
+cheeseburger,
+hotdog, hot dog, red hot,
+mashed potato,
+head cabbage,
+broccoli,
+cauliflower,
+zucchini, courgette,
+spaghetti squash,
+acorn squash,
+butternut squash,
+cucumber, cuke,
+artichoke, globe artichoke,
+bell pepper,
+cardoon,
+mushroom,
+Granny Smith,
+strawberry,
+orange,
+lemon,
+fig,
+pineapple, ananas,
+banana,
+jackfruit, jak, jack,
+custard apple,
+pomegranate,
+hay,
+carbonara,
+chocolate sauce, chocolate syrup,
+dough,
+meat loaf, meatloaf,
+pizza, pizza pie,
+potpie,
+burrito,
+red wine,
+espresso,
+cup,
+eggnog,
+alp,
+bubble,
+cliff, drop, drop-off,
+coral reef,
+geyser,
+lakeside, lakeshore,
+promontory, headland, head, foreland,
+sandbar, sand bar,
+seashore, coast, seacoast, sea-coast,
+valley, vale,
+volcano,
+ballplayer, baseball player,
+groom, bridegroom,
+scuba diver,
+rapeseed,
+daisy,
+"yellow ladys slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",
+corn,
+acorn,
+hip, rose hip, rosehip,
+buckeye, horse chestnut, conker,
+coral fungus,
+agaric,
+gyromitra,
+stinkhorn, carrion fungus,
+earthstar,
+hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa,
+bolete,
+ear, spike, capitulum,
+toilet tissue, toilet paper, bathroom tissue

python/examples/imagenet/image_rpc_client.py → python/examples/imagenet/resnet50_rpc_client.py

@@ -14,23 +14,35 @@
 
 import sys
 from paddle_serving_client import Client
-from paddle_serving_app.reader import Sequential, File2Image, Resize, CenterCrop, RGB2BGR, Transpose, Div, Normalize
+from paddle_serving_app.reader import Sequential, URL2Image, Resize
+from paddle_serving_app.reader import CenterCrop, RGB2BGR, Transpose, Div, Normalize
 import time
 
 client = Client()
 client.load_client_config(sys.argv[1])
-client.connect(["127.0.0.1:9393"])
+client.connect(["127.0.0.1:9696"])
+
+label_dict = {}
+label_idx = 0
+with open("imagenet.label") as fin:
+    for line in fin:
+        label_dict[label_idx] = line.strip()
+        label_idx += 1
 
 seq = Sequential([
-    File2Image(), Resize(256), CenterCrop(224), RGB2BGR(), Transpose((2, 0, 1)),
-    Div(255), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    URL2Image(), Resize(256), CenterCrop(224), RGB2BGR(), Transpose((2, 0, 1)),
+    Div(255), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True)
 ])
-print(seq)
 
 start = time.time()
-image_file = "daisy.jpg"
-for i in range(1000):
+image_file = "https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg"
+for i in range(10):
     img = seq(image_file)
     fetch_map = client.predict(feed={"image": img}, fetch=["score"])
+    prob = max(fetch_map["score"][0])
+    label = label_dict[fetch_map["score"][0].tolist().index(prob)].strip(
+    ).replace(",", "")
+    print("prediction: {}, probability: {}".format(label, prob))
+
 end = time.time()
 print(end - start)

python/examples/imagenet/image_classification_service.py → python/examples/imagenet/resnet50_web_service.py

@@ -11,31 +11,62 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
-from paddle_serving_server.web_service import WebService
 import sys
-import cv2
-import base64
-import numpy as np
-from paddle_serving_app import ImageReader
+from paddle_serving_client import Client
+from paddle_serving_app.reader import Sequential, URL2Image, Resize, CenterCrop, RGB2BGR, Transpose, Div, Normalize
+
+if len(sys.argv) != 4:
+    print("python resnet50_web_service.py model device port")
+    sys.exit(-1)
+
+device = sys.argv[2]
+
+if device == "cpu":
+    from paddle_serving_server.web_service import WebService
+else:
+    from paddle_serving_server_gpu.web_service import WebService
 
 
 class ImageService(WebService):
-    def preprocess(self, feed={}, fetch=[]):
-        reader = ImageReader()
+    def init_imagenet_setting(self):
+        self.seq = Sequential([
+            URL2Image(), Resize(256), CenterCrop(224), RGB2BGR(), Transpose(
+                (2, 0, 1)), Div(255), Normalize([0.485, 0.456, 0.406],
+                                                [0.229, 0.224, 0.225], True)
+        ])
+        self.label_dict = {}
+        label_idx = 0
+        with open("imagenet.label") as fin:
+            for line in fin:
+                self.label_dict[label_idx] = line.strip()
+                label_idx += 1
+
+    def preprocess(self, feed=[], fetch=[]):
         feed_batch = []
         for ins in feed:
             if "image" not in ins:
                 raise ("feed data error!")
-            sample = base64.b64decode(ins["image"])
-            img = reader.process_image(sample)
+            img = self.seq(ins["image"])
             feed_batch.append({"image": img})
         return feed_batch, fetch
 
+    def postprocess(self, feed=[], fetch=[], fetch_map={}):
+        score_list = fetch_map["score"]
+        result = {"label": [], "prob": []}
+        for score in score_list:
+            max_score = max(score)
+            result["label"].append(self.label_dict[score.index(max_score)]
+                                   .strip().replace(",", ""))
+            result["prob"].append(max_score)
+        return result
+
 
 image_service = ImageService(name="image")
 image_service.load_model_config(sys.argv[1])
+image_service.init_imagenet_setting()
+if device == "gpu":
+    image_service.set_gpus("0,1")
 image_service.prepare_server(
-    workdir=sys.argv[2], port=int(sys.argv[3]), device="cpu")
+    workdir="workdir", port=int(sys.argv[3]), device=device)
 image_service.run_server()
 image_service.run_flask()

python/examples/imdb/benchmark.py

@@ -16,7 +16,7 @@
 import sys
 import time
 import requests
-from imdb_reader import IMDBDataset
+from paddle_serving_app import IMDBDataset
 from paddle_serving_client import Client
 from paddle_serving_client.utils import MultiThreadRunner
 from paddle_serving_client.utils import benchmark_args
@@ -37,26 +37,39 @@ def single_func(idx, resource):
         client.load_client_config(args.model)
         client.connect([args.endpoint])
         for i in range(1000):
-            if args.batch_size == 1:
-                word_ids, label = imdb_dataset.get_words_and_label(line)
-                fetch_map = client.predict(
-                    feed={"words": word_ids}, fetch=["prediction"])
+            if args.batch_size >= 1:
+                feed_batch = []
+                for bi in range(args.batch_size):
+                    word_ids, label = imdb_dataset.get_words_and_label(dataset[
+                        bi])
+                    feed_batch.append({"words": word_ids})
+                result = client.predict(feed=feed_batch, fetch=["prediction"])
+                if result is None:
+                    raise ("predict failed.")
             else:
                 print("unsupport batch size {}".format(args.batch_size))
 
     elif args.request == "http":
-        for fn in filelist:
-            fin = open(fn)
-            for line in fin:
-                word_ids, label = imdb_dataset.get_words_and_label(line)
-                r = requests.post(
-                    "http://{}/imdb/prediction".format(args.endpoint),
-                    data={"words": word_ids,
-                          "fetch": ["prediction"]})
+        if args.batch_size >= 1:
+            feed_batch = []
+            for bi in range(args.batch_size):
+                feed_batch.append({"words": dataset[bi]})
+            r = requests.post(
+                "http://{}/imdb/prediction".format(args.endpoint),
+                json={"feed": feed_batch,
+                      "fetch": ["prediction"]})
+            if r.status_code != 200:
+                print('HTTP status code -ne 200')
+                raise ("predict failed.")
+        else:
+            print("unsupport batch size {}".format(args.batch_size))
     end = time.time()
     return [[end - start]]
 
 
 multi_thread_runner = MultiThreadRunner()
 result = multi_thread_runner.run(single_func, args.thread, {})
-print(result)
+avg_cost = 0
+for cost in result[0]:
+    avg_cost += cost
+print("total cost {} s of each thread".format(avg_cost / args.thread))

python/examples/imdb/benchmark.sh

 rm profile_log
 for thread_num in 1 2 4 8 16
 do
-    $PYTHONROOT/bin/python benchmark.py --thread $thread_num --model imdbo_bow_client_conf/serving_client_conf.prototxt --request rpc > profile 2>&1
+for batch_size in 1 2 4 8 16 32 64 128 256 512
+do
+    $PYTHONROOT/bin/python benchmark.py --thread $thread_num --batch_size $batch_size --model imdb_bow_client_conf/serving_client_conf.prototxt --request rpc > profile 2>&1
     echo "========================================"
     echo "batch size : $batch_size" >> profile_log
     $PYTHONROOT/bin/python ../util/show_profile.py profile $thread_num >> profile_log
     tail -n 1 profile >> profile_log
 done
+done

python/examples/imdb/benchmark_batch.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# pylint: disable=doc-string-missing
-
-import sys
-import time
-import requests
-from imdb_reader import IMDBDataset
-from paddle_serving_client import Client
-from paddle_serving_client.utils import MultiThreadRunner
-from paddle_serving_client.utils import benchmark_args
-
-args = benchmark_args()
-
-
-def single_func(idx, resource):
-    imdb_dataset = IMDBDataset()
-    imdb_dataset.load_resource("./imdb.vocab")
-    dataset = []
-    with open("./test_data/part-0") as fin:
-        for line in fin:
-            dataset.append(line.strip())
-    start = time.time()
-    if args.request == "rpc":
-        client = Client()
-        client.load_client_config(args.model)
-        client.connect([args.endpoint])
-        for i in range(1000):
-            if args.batch_size >= 1:
-                feed_batch = []
-                for bi in range(args.batch_size):
-                    word_ids, label = imdb_dataset.get_words_and_label(dataset[
-                        bi])
-                    feed_batch.append({"words": word_ids})
-                result = client.predict(feed=feed_batch, fetch=["prediction"])
-                if result is None:
-                    raise ("predict failed.")
-            else:
-                print("unsupport batch size {}".format(args.batch_size))
-
-    elif args.request == "http":
-        if args.batch_size >= 1:
-            feed_batch = []
-            for bi in range(args.batch_size):
-                feed_batch.append({"words": dataset[bi]})
-            r = requests.post(
-                "http://{}/imdb/prediction".format(args.endpoint),
-                json={"feed": feed_batch,
-                      "fetch": ["prediction"]})
-            if r.status_code != 200:
-                print('HTTP status code -ne 200')
-                raise ("predict failed.")
-        else:
-            print("unsupport batch size {}".format(args.batch_size))
-    end = time.time()
-    return [[end - start]]
-
-
-multi_thread_runner = MultiThreadRunner()
-result = multi_thread_runner.run(single_func, args.thread, {})
-avg_cost = 0
-for cost in result[0]:
-    avg_cost += cost
-print("total cost {} s of each thread".format(avg_cost / args.thread))

python/examples/imdb/benchmark_batch.sh

-rm profile_log
-for thread_num in 1 2 4 8 16
-do
-for batch_size in 1 2 4 8 16 32 64 128 256 512
-do
-    $PYTHONROOT/bin/python benchmark_batch.py --thread $thread_num --batch_size $batch_size --model imdb_bow_client_conf/serving_client_conf.prototxt --request rpc > profile 2>&1
-    echo "========================================"
-    echo "batch size : $batch_size" >> profile_log
-    $PYTHONROOT/bin/python ../util/show_profile.py profile $thread_num >> profile_log
-    tail -n 1 profile >> profile_log
-done
-done

python/examples/imdb/test_client.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # pylint: disable=doc-string-missing
 from paddle_serving_client import Client
-from imdb_reader import IMDBDataset
+from paddle_serving_app import IMDBDataset
 import sys
 
 client = Client()

python/examples/imdb/test_client_batch.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# pylint: disable=doc-string-missing
-
-from paddle_serving_client import Client
-import sys
-import subprocess
-from multiprocessing import Pool
-import time
-
-
-def batch_predict(batch_size=4):
-    client = Client()
-    client.load_client_config(conf_file)
-    client.connect(["127.0.0.1:9292"])
-    fetch = ["acc", "cost", "prediction"]
-    feed_batch = []
-    for line in sys.stdin:
-        group = line.strip().split()
-        words = [int(x) for x in group[1:int(group[0])]]
-        label = [int(group[-1])]
-        feed = {"words": words, "label": label}
-        feed_batch.append(feed)
-        if len(feed_batch) == batch_size:
-            fetch_batch = client.batch_predict(
-                feed_batch=feed_batch, fetch=fetch)
-            for i in range(batch_size):
-                print("{} {}".format(fetch_batch[i]["prediction"][1],
-                                     feed_batch[i]["label"][0]))
-            feed_batch = []
-    if len(feed_batch) > 0:
-        fetch_batch = client.batch_predict(feed_batch=feed_batch, fetch=fetch)
-        for i in range(len(feed_batch)):
-            print("{} {}".format(fetch_batch[i]["prediction"][1], feed_batch[i][
-                "label"][0]))
-
-
-if __name__ == '__main__':
-    conf_file = sys.argv[1]
-    batch_size = int(sys.argv[2])
-    batch_predict(batch_size)

python/examples/imdb/text_classify_service.py

@@ -14,7 +14,7 @@
 # pylint: disable=doc-string-missing
 
 from paddle_serving_server.web_service import WebService
-from imdb_reader import IMDBDataset
+from paddle_serving_app import IMDBDataset
 import sys
 
 

python/examples/senta/senta_web_service.py

@@ -51,13 +51,11 @@ class SentaService(WebService):
     def init_lac_service(self):
         ps = Process(target=self.start_lac_service())
         ps.start()
-        #self.init_lac_client()
+        self.init_lac_client()
 
     def lac_predict(self, feed_data):
-        self.init_lac_client()
         lac_result = self.lac_client.predict(
             feed={"words": feed_data}, fetch=["crf_decode"])
-        self.lac_client.release()
         return lac_result
 
     def init_lac_client(self):

python/paddle_serving_app/__init__.py

@@ -12,8 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from .reader.chinese_bert_reader import ChineseBertReader
-from .reader.image_reader import ImageReader, File2Image, URL2Image, Sequential, Normalize, CenterCrop, Resize
+from .reader.image_reader import ImageReader, File2Image, URL2Image, Sequential, Normalize, CenterCrop, Resize, PadStride
 from .reader.lac_reader import LACReader
 from .reader.senta_reader import SentaReader
+from .reader.imdb_reader import IMDBDataset
 from .models import ServingModels
 from .local_predict import Debugger

python/paddle_serving_app/reader/__init__.py

@@ -11,4 +11,4 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from .image_reader import ImageReader, File2Image, URL2Image, Sequential, Normalize, CenterCrop, Resize, Transpose, Div, RGB2BGR, BGR2RGB, RCNNPostprocess, SegPostprocess
+from .image_reader import ImageReader, File2Image, URL2Image, Sequential, Normalize, CenterCrop, Resize, Transpose, Div, RGB2BGR, BGR2RGB, RCNNPostprocess, SegPostprocess, PadStride

python/paddle_serving_app/reader/image_reader.py

@@ -465,6 +465,24 @@ class Resize(object):
             _cv2_interpolation_to_str[self.interpolation])
 
 
+class PadStride(object):
+    def __init__(self, stride):
+        self.coarsest_stride = stride
+
+    def __call__(self, img):
+        coarsest_stride = self.coarsest_stride
+        if coarsest_stride == 0:
+            return img
+        im_c, im_h, im_w = img.shape
+        pad_h = int(np.ceil(float(im_h) / coarsest_stride) * coarsest_stride)
+        pad_w = int(np.ceil(float(im_w) / coarsest_stride) * coarsest_stride)
+        padding_im = np.zeros((im_c, pad_h, pad_w), dtype=np.float32)
+        padding_im[:, :im_h, :im_w] = img
+        im_info = {}
+        im_info['resize_shape'] = padding_im.shape[1:]
+        return padding_im
+
+
 class Transpose(object):
     def __init__(self, transpose_target):
         self.transpose_target = transpose_target

python/paddle_serving_app/reader/imdb_reader.py

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# pylint: disable=doc-string-missing
+
+import sys
+import os
+import paddle
+import re
+import paddle.fluid.incubate.data_generator as dg
+
+py_version = sys.version_info[0]
+
+
+class IMDBDataset(dg.MultiSlotDataGenerator):
+    def load_resource(self, dictfile):
+        self._vocab = {}
+        wid = 0
+        if py_version == 2:
+            with open(dictfile) as f:
+                for line in f:
+                    self._vocab[line.strip()] = wid
+                    wid += 1
+        else:
+            with open(dictfile, encoding="utf-8") as f:
+                for line in f:
+                    self._vocab[line.strip()] = wid
+                    wid += 1
+        self._unk_id = len(self._vocab)
+        self._pattern = re.compile(r'(;|,|\.|\?|!|\s|\(|\))')
+        self.return_value = ("words", [1, 2, 3, 4, 5, 6]), ("label", [0])
+
+    def get_words_only(self, line):
+        sent = line.lower().replace("<br />", " ").strip()
+        words = [x for x in self._pattern.split(sent) if x and x != " "]
+        feas = [
+            self._vocab[x] if x in self._vocab else self._unk_id for x in words
+        ]
+        return feas
+
+    def get_words_and_label(self, line):
+        send = '|'.join(line.split('|')[:-1]).lower().replace("<br />",
+                                                              " ").strip()
+        label = [int(line.split('|')[-1])]
+
+        words = [x for x in self._pattern.split(send) if x and x != " "]
+        feas = [
+            self._vocab[x] if x in self._vocab else self._unk_id for x in words
+        ]
+        return feas, label
+
+    def infer_reader(self, infer_filelist, batch, buf_size):
+        def local_iter():
+            for fname in infer_filelist:
+                with open(fname, "r") as fin:
+                    for line in fin:
+                        feas, label = self.get_words_and_label(line)
+                        yield feas, label
+
+        import paddle
+        batch_iter = paddle.batch(
+            paddle.reader.shuffle(
+                local_iter, buf_size=buf_size),
+            batch_size=batch)
+        return batch_iter
+
+    def generate_sample(self, line):
+        def memory_iter():
+            for i in range(1000):
+                yield self.return_value
+
+        def data_iter():
+            feas, label = self.get_words_and_label(line)
+            yield ("words", feas), ("label", label)
+
+        return data_iter
+
+
+if __name__ == "__main__":
+    imdb = IMDBDataset()
+    imdb.load_resource("imdb.vocab")
+    imdb.run_from_stdin()

python/paddle_serving_client/__init__.py

@@ -329,9 +329,9 @@ class Client(object):
             # result map needs to be a numpy array
             for i, name in enumerate(fetch_names):
                 if self.fetch_names_to_type_[name] == int_type:
+                    # result_map[name] will be py::array(numpy array)
                     result_map[name] = result_batch.get_int64_by_name(mi, name)
                     shape = result_batch.get_shape(mi, name)
-                    result_map[name] = np.array(result_map[name], dtype='int64')
                     result_map[name].shape = shape
                     if name in self.lod_tensor_set:
                         result_map["{}.lod".format(name)] = np.array(
@@ -339,8 +339,6 @@ class Client(object):
                 elif self.fetch_names_to_type_[name] == float_type:
                     result_map[name] = result_batch.get_float_by_name(mi, name)
                     shape = result_batch.get_shape(mi, name)
-                    result_map[name] = np.array(
-                        result_map[name], dtype='float32')
                     result_map[name].shape = shape
                     if name in self.lod_tensor_set:
                         result_map["{}.lod".format(name)] = np.array(

python/paddle_serving_client/io/__init__.py

@@ -104,10 +104,10 @@ def save_model(server_model_folder,
 
 
 def inference_model_to_serving(dirname,
-                               model_filename=None,
-                               params_filename=None,
                                serving_server="serving_server",
-                               serving_client="serving_client"):
+                               serving_client="serving_client",
+                               model_filename=None,
+                               params_filename=None):
     place = fluid.CPUPlace()
     exe = fluid.Executor(place)
     inference_program, feed_target_names, fetch_targets = \

python/paddle_serving_server/__init__.py

@@ -274,7 +274,8 @@ class Server(object):
                 self.model_config_paths[node.name] = path
             print("You have specified multiple model paths, please ensure "
                   "that the input and output of multiple models are the same.")
-            workflow_oi_config_path = self.model_config_paths.items()[0][1]
+            workflow_oi_config_path = list(self.model_config_paths.items())[0][
+                1]
         else:
             raise Exception("The type of model_config_paths must be str or "
                             "dict({op: model_path}), not {}.".format(

python/paddle_serving_server/web_service.py

@@ -101,7 +101,6 @@ class WebService(object):
         p_rpc = Process(target=self._launch_rpc_service)
         p_rpc.start()
 
-    def run_flask(self):
         app_instance = Flask(__name__)
 
         @app_instance.before_first_request
@@ -114,10 +113,16 @@ class WebService(object):
         def run():
             return self.get_prediction(request)
 
-        app_instance.run(host="0.0.0.0",
-                         port=self.port,
-                         threaded=False,
-                         processes=4)
+        self.app_instance = app_instance
+
+    def run_flask(self):
+        self.app_instance.run(host="0.0.0.0",
+                              port=self.port,
+                              threaded=False,
+                              processes=1)
+
+    def get_app_instance(self):
+        return self.app_instance
 
     def preprocess(self, feed=[], fetch=[]):
         return feed, fetch

python/paddle_serving_server_gpu/__init__.py

@@ -320,7 +320,8 @@ class Server(object):
                 self.model_config_paths[node.name] = path
             print("You have specified multiple model paths, please ensure "
                   "that the input and output of multiple models are the same.")
-            workflow_oi_config_path = self.model_config_paths.items()[0][1]
+            workflow_oi_config_path = list(self.model_config_paths.items())[0][
+                1]
         else:
             raise Exception("The type of model_config_paths must be str or "
                             "dict({op: model_path}), not {}.".format(

python/paddle_serving_server_gpu/web_service.py

@@ -151,7 +151,6 @@ class WebService(object):
         for p in server_pros:
             p.start()
 
-    def run_flask(self):
         app_instance = Flask(__name__)
 
         @app_instance.before_first_request
@@ -164,10 +163,16 @@ class WebService(object):
         def run():
             return self.get_prediction(request)
 
-        app_instance.run(host="0.0.0.0",
-                         port=self.port,
-                         threaded=False,
-                         processes=4)
+        self.app_instance = app_instance
+
+    def run_flask(self):
+        self.app_instance.run(host="0.0.0.0",
+                              port=self.port,
+                              threaded=False,
+                              processes=1)
+
+    def get_app_instance(self):
+        return app_instance
 
     def preprocess(self, feed=[], fetch=[]):
         return feed, fetch

tools/Dockerfile.centos6.devel

@@ -43,5 +43,5 @@ RUN yum -y install wget && \
     source /root/.bashrc && \
     cd .. && rm -rf Python-3.6.8* && \
     pip3 install google protobuf setuptools wheel flask numpy==1.16.4 && \
-    yum -y install epel-release && yum -y install patchelf && \
+    yum -y install epel-release && yum -y install patchelf libXext libSM libXrender && \
     yum clean all

tools/Dockerfile.centos6.gpu.devel

@@ -43,5 +43,5 @@ RUN yum -y install wget && \
     source /root/.bashrc && \
     cd .. && rm -rf Python-3.6.8* && \
     pip3 install google protobuf setuptools wheel flask numpy==1.16.4 && \
-    yum -y install epel-release && yum -y install patchelf && \
+    yum -y install epel-release && yum -y install patchelf libXext libSM libXrender && \
     yum clean all

tools/Dockerfile.devel

@@ -20,5 +20,5 @@ RUN yum -y install wget >/dev/null \
     && rm get-pip.py \
     && yum install -y python3 python3-devel \
     && pip3 install google protobuf setuptools wheel flask \
-    && yum -y install epel-release && yum -y install patchelf \
+    && yum -y install epel-release && yum -y install patchelf libXext libSM libXrender\
     && yum clean all

tools/Dockerfile.gpu.devel

@@ -21,5 +21,5 @@ RUN yum -y install wget >/dev/null \
     && rm get-pip.py \
     && yum install -y python3 python3-devel \
     && pip3 install google protobuf setuptools wheel flask \
-    && yum -y install epel-release && yum -y install patchelf \
+    && yum -y install epel-release && yum -y install patchelf libXext libSM libXrender\
     && yum clean all

tools/serving_build.sh

@@ -343,7 +343,7 @@ function python_test_imdb() {
             sleep 5
             check_cmd "head test_data/part-0 | python test_client.py imdb_cnn_client_conf/serving_client_conf.prototxt imdb.vocab"
             # test batch predict
-            check_cmd "python benchmark_batch.py --thread 4 --batch_size 8 --model imdb_bow_client_conf/serving_client_conf.prototxt --request rpc --endpoint 127.0.0.1:9292"
+            check_cmd "python benchmark.py --thread 4 --batch_size 8 --model imdb_bow_client_conf/serving_client_conf.prototxt --request rpc --endpoint 127.0.0.1:9292"
             echo "imdb CPU RPC inference pass"
             kill_server_process
             rm -rf work_dir1
@@ -359,7 +359,7 @@ function python_test_imdb() {
                 exit 1
             fi
             # test batch predict
-            check_cmd "python benchmark_batch.py --thread 4 --batch_size 8 --model imdb_bow_client_conf/serving_client_conf.prototxt --request http --endpoint 127.0.0.1:9292"
+            check_cmd "python benchmark.py --thread 4 --batch_size 8 --model imdb_bow_client_conf/serving_client_conf.prototxt --request http --endpoint 127.0.0.1:9292"
             setproxy # recover proxy state
             kill_server_process
             ps -ef | grep "text_classify_service.py" | grep -v grep | awk '{print $2}' | xargs kill