fix code style

core/general-server/op/general_reader_op.cpp

@@ -91,7 +91,7 @@ int GeneralReaderOp::inference() {
   capacity.resize(var_num);
   for (int i = 0; i < var_num; ++i) {
     std::string tensor_name = model_config->_feed_name[i];
-    VLOG(2) <<  "(logid=" << log_id << ") get tensor name: " << tensor_name;
+    VLOG(2) << "(logid=" << log_id << ") get tensor name: " << tensor_name;
     auto lod_tensor = InferManager::instance().GetInputHandle(
         engine_name.c_str(), tensor_name.c_str());
     std::vector<std::vector<size_t>> lod;

doc/LATEST_PACKAGES.md

@@ -77,4 +77,3 @@ https://paddle-serving.bj.bcebos.com/whl/xpu/paddle_serving_client-0.0.0-cp36-no
 # App
 https://paddle-serving.bj.bcebos.com/whl/xpu/paddle_serving_app-0.0.0-py3-none-any.whl 
 ```
-

java/README.md

@@ -115,5 +115,3 @@ The second is to deploy GPU Serving and Java Client separately. If they are on t
 **Currently Serving has launched the Pipeline mode (see [Pipeline Serving](../doc/PIPELINE_SERVING.md) for details). Pipeline Serving Client for Java is released.**
 
 **It should be noted that in the example, Java Pipeline Client code is in path /Java/Examples and /Java/src/main, and the Pipeline server code is in path /python/examples/pipeline/    The Client IP and Port(which is configured in java/examples/src/main/java/PipelineClientExample.java) should be corresponding to the Pipeline Server IP and Port(which is configured in config.yaml) **
-
-

paddle_inference/inferencer-fluid-cpu/include/fluid_cpu_engine.h

@@ -295,7 +295,7 @@ class FluidCpuAnalysisEncryptCore : public FluidFamilyCore {
     std::string real_params_buffer = cipher->Decrypt(params_buffer, key_buffer);
 
     Config analysis_config;
-    //paddle::AnalysisConfig analysis_config;
+    // paddle::AnalysisConfig analysis_config;
     analysis_config.SetModelBuffer(&real_model_buffer[0],
                                    real_model_buffer.size(),
                                    &real_params_buffer[0],
@@ -308,8 +308,7 @@ class FluidCpuAnalysisEncryptCore : public FluidFamilyCore {
     analysis_config.SwitchSpecifyInputNames(true);
     AutoLock lock(GlobalPaddleCreateMutex::instance());
     VLOG(2) << "decrypt model file sucess";
-    _core =
-        CreatePredictor(analysis_config);
+    _core = CreatePredictor(analysis_config);
     if (NULL == _core.get()) {
       LOG(ERROR) << "create paddle predictor failed, path: " << data_path;
       return -1;

paddle_inference/inferencer-fluid-gpu/include/fluid_gpu_engine.h

@@ -283,7 +283,6 @@ class Parameter {
   float* _params;
 };
 
-
 class FluidGpuAnalysisEncryptCore : public FluidFamilyCore {
  public:
   void ReadBinaryFile(const std::string& filename, std::string* contents) {
@@ -328,8 +327,7 @@ class FluidGpuAnalysisEncryptCore : public FluidFamilyCore {
     analysis_config.SwitchSpecifyInputNames(true);
     AutoLock lock(GlobalPaddleCreateMutex::instance());
     VLOG(2) << "decrypt model file sucess";
-    _core =
-        CreatePredictor(analysis_config);
+    _core = CreatePredictor(analysis_config);
     if (NULL == _core.get()) {
       LOG(ERROR) << "create paddle predictor failed, path: " << data_path;
       return -1;
@@ -339,7 +337,6 @@ class FluidGpuAnalysisEncryptCore : public FluidFamilyCore {
   }
 };
 
-
 }  // namespace fluid_gpu
 }  // namespace paddle_serving
 }  // namespace baidu

python/examples/grpc_impl_example/fit_a_line/README_CN.md

@@ -43,4 +43,3 @@ python test_batch_client.py
 ``` shell
 python test_timeout_client.py
 ```
-

python/examples/grpc_impl_example/fit_a_line/test_asyn_client.py

@@ -43,8 +43,9 @@ x = [
 ]
 task_count = 0
 for i in range(3):
-    new_data = np.array(x).astype("float32").reshape((1,13))
-    future = client.predict(feed={"x": new_data}, fetch=["price"], batch=False, asyn=True)
+    new_data = np.array(x).astype("float32").reshape((1, 13))
+    future = client.predict(
+        feed={"x": new_data}, fetch=["price"], batch=False, asyn=True)
     task_count += 1
     future.add_done_callback(functools.partial(call_back))
 

python/examples/grpc_impl_example/fit_a_line/test_batch_client.py

@@ -27,7 +27,8 @@ for i in range(3):
     new_data = np.array(x).astype("float32").reshape((1, 1, 13))
     batch_data = np.concatenate([new_data, new_data, new_data], axis=0)
     print(batch_data.shape)
-    fetch_map = client.predict(feed={"x":batch_data}, fetch=["price"], batch=True)
+    fetch_map = client.predict(
+        feed={"x": batch_data}, fetch=["price"], batch=True)
 
     if fetch_map["serving_status_code"] == 0:
         print(fetch_map)

python/examples/grpc_impl_example/fit_a_line/test_sync_client.py

@@ -17,7 +17,6 @@ from paddle_serving_client import MultiLangClient as Client
 import numpy as np
 client = Client()
 client.connect(["127.0.0.1:9393"])
-
 """
 for data in test_reader():
     new_data = np.zeros((1, 1, 13)).astype("float32")
@@ -33,8 +32,9 @@ x = [
     0.4919, 0.1856, 0.0795, -0.0332
 ]
 for i in range(3):
-    new_data = np.array(x).astype("float32").reshape((1,13))
-    fetch_map = client.predict(feed={"x": new_data}, fetch=["price"], batch=False)
+    new_data = np.array(x).astype("float32").reshape((1, 13))
+    fetch_map = client.predict(
+        feed={"x": new_data}, fetch=["price"], batch=False)
     if fetch_map["serving_status_code"] == 0:
         print(fetch_map)
     else:

python/examples/grpc_impl_example/fit_a_line/test_timeout_client.py

@@ -25,8 +25,9 @@ x = [
     0.4919, 0.1856, 0.0795, -0.0332
 ]
 for i in range(3):
-    new_data = np.array(x).astype("float32").reshape((1,13))
-    fetch_map = client.predict(feed={"x": new_data}, fetch=["price"], batch=False)
+    new_data = np.array(x).astype("float32").reshape((1, 13))
+    fetch_map = client.predict(
+        feed={"x": new_data}, fetch=["price"], batch=False)
     if fetch_map["serving_status_code"] == 0:
         print(fetch_map)
     elif fetch_map["serving_status_code"] == grpc.StatusCode.DEADLINE_EXCEEDED:

python/examples/grpc_impl_example/yolov4/test_client.py

@@ -35,7 +35,8 @@ fetch_map = client.predict(
         "image": im,
         "im_size": np.array(list(im.shape[1:])),
     },
-    fetch=["save_infer_model/scale_0.tmp_0"], batch=False)
+    fetch=["save_infer_model/scale_0.tmp_0"],
+    batch=False)
 print(fetch_map)
 fetch_map.pop("serving_status_code")
 fetch_map["image"] = sys.argv[1]

python/examples/pipeline/simple_web_service/web_service_java.py

@@ -23,6 +23,8 @@ import base64
 
 _LOGGER = logging.getLogger()
 np.set_printoptions(threshold=sys.maxsize)
+
+
 class UciOp(Op):
     def init_op(self):
         self.separator = ","
@@ -38,8 +40,8 @@ class UciOp(Op):
             log_id, input_dict))
         proc_dict = {}
         x_value = input_dict["x"]
-        input_dict["x"] = x_value.reshape(1,13)
-        
+        input_dict["x"] = x_value.reshape(1, 13)
+
         return input_dict, False, None, ""
 
     def postprocess(self, input_dicts, fetch_dict, log_id):

python/paddle_serving_client/__init__.py

@@ -228,7 +228,7 @@ class Client(object):
                     "You must set the endpoints parameter or use add_variant function to create a variant."
                 )
         else:
-	    if encryption:
+            if encryption:
                 endpoints = self.get_serving_port(endpoints)
             if self.predictor_sdk_ is None:
                 self.add_variant('default_tag_{}'.format(id(self)), endpoints,

python/paddle_serving_client/io/__init__.py

@@ -31,20 +31,24 @@ import paddle.nn.functional as F
 import errno
 from paddle.jit import to_static
 
+
 def save_dygraph_model(serving_model_folder, client_config_folder, model):
     paddle.jit.save(model, "serving_tmp")
-    loaded_layer = paddle.jit.load(path=".", model_filename="serving_tmp.pdmodel", params_filename="serving_tmp.pdiparams")
+    loaded_layer = paddle.jit.load(
+        path=".",
+        model_filename="serving_tmp.pdmodel",
+        params_filename="serving_tmp.pdiparams")
     feed_target_names = [x.name for x in loaded_layer._input_spec()]
     fetch_target_names = [x.name for x in loaded_layer._output_spec()]
 
     inference_program = loaded_layer.program()
     feed_var_dict = {
-           x: inference_program.global_block().var(x)
-           for x in feed_target_names
+        x: inference_program.global_block().var(x)
+        for x in feed_target_names
     }
     fetch_var_dict = {
-           x: inference_program.global_block().var(x)
-           for x in fetch_target_names
+        x: inference_program.global_block().var(x)
+        for x in fetch_target_names
     }
     config = model_conf.GeneralModelConfig()
 
@@ -93,9 +97,11 @@ def save_dygraph_model(serving_model_folder, client_config_folder, model):
     os.system(cmd)
     cmd = "mkdir -p {}".format(serving_model_folder)
     os.system(cmd)
-    cmd = "mv {} {}/__model__".format("serving_tmp.pdmodel", serving_model_folder)
+    cmd = "mv {} {}/__model__".format("serving_tmp.pdmodel",
+                                      serving_model_folder)
     os.system(cmd)
-    cmd = "mv {} {}/__params__".format("serving_tmp.pdiparams", serving_model_folder)
+    cmd = "mv {} {}/__params__".format("serving_tmp.pdiparams",
+                                       serving_model_folder)
     os.system(cmd)
     cmd = "rm -rf serving_tmp.pd*"
     os.system(cmd)
@@ -112,11 +118,12 @@ def save_dygraph_model(serving_model_folder, client_config_folder, model):
             serving_model_folder), "wb") as fout:
         fout.write(config.SerializeToString())
 
+
 def save_model(server_model_folder,
                client_config_folder,
                feed_var_dict,
                fetch_var_dict,
-	       main_program=None,
+               main_program=None,
                encryption=False,
                key_len=128,
                encrypt_conf=None):
@@ -130,7 +137,7 @@ def save_model(server_model_folder,
         target_var_names.append(key)
 
     if not encryption:
-	save_inference_model(
+        save_inference_model(
             server_model_folder,
             feed_var_names,
             target_vars,

python/paddle_serving_server/__init__.py

@@ -158,7 +158,7 @@ class Server(object):
         self.use_local_bin = False
         self.mkl_flag = False
         self.encryption_model = False
-	self.product_name = None
+        self.product_name = None
         self.container_id = None
         self.model_config_paths = None  # for multi-model in a workflow
 
@@ -197,6 +197,7 @@ class Server(object):
 
     def set_ir_optimize(self, flag=False):
         self.ir_optimization = flag
+
     def use_encryption_model(self, flag=False):
         self.encryption_model = flag
 
@@ -236,15 +237,15 @@ class Server(object):
             if os.path.exists('{}/__params__'.format(model_config_path)):
                 suffix = ""
             else:
-                suffix = "_DIR" 
+                suffix = "_DIR"
 
             if device == "cpu":
-		if self.encryption_model:
+                if self.encryption_model:
                     engine.type = "FLUID_CPU_ANALYSIS_ENCRYPT"
                 else:
                     engine.type = "FLUID_CPU_ANALYSIS" + suffix
             elif device == "gpu":
-		if self.encryption_model:
+                if self.encryption_model:
                     engine.type = "FLUID_GPU_ANALYSIS_ENCRYPT"
                 else:
                     engine.type = "FLUID_GPU_ANALYSIS" + suffix

python/paddle_serving_server/serve.py

@@ -133,6 +133,7 @@ def start_standard_model(serving_port):  # pylint: disable=doc-string-missing
     server.prepare_server(workdir=workdir, port=port, device=device)
     server.run_server()
 
+
 class MainService(BaseHTTPRequestHandler):
     def get_available_port(self):
         default_port = 12000
@@ -200,6 +201,7 @@ class MainService(BaseHTTPRequestHandler):
         self.end_headers()
         self.wfile.write(json.dumps(response))
 
+
 if __name__ == "__main__":
 
     args = parse_args()

python/paddle_serving_server/web_service.py

@@ -120,7 +120,7 @@ class WebService(object):
         self.mem_optim = mem_optim
         self.ir_optim = ir_optim
         for i in range(1000):
-	    if port_is_available(default_port + i):
+            if port_is_available(default_port + i):
                 self.port_list.append(default_port + i)
                 break
 
@@ -216,10 +216,12 @@ class WebService(object):
             feed_dict[var_name] = []
         for feed_ins in feed:
             for key in feed_ins:
-                feed_dict[key].append(np.array(feed_ins[key]).reshape(list(self.feed_vars[key].shape))[np.newaxis,:])
+                feed_dict[key].append(
+                    np.array(feed_ins[key]).reshape(
+                        list(self.feed_vars[key].shape))[np.newaxis, :])
         feed = {}
         for key in feed_dict:
-            feed[key] = np.concatenate(feed_dict[key], axis=0) 
+            feed[key] = np.concatenate(feed_dict[key], axis=0)
         return feed, fetch, is_batch
 
     def postprocess(self, feed=[], fetch=[], fetch_map=None):

python/paddle_serving_server_gpu/__init__.py

@@ -323,20 +323,20 @@ class Server(object):
             if os.path.exists('{}/__params__'.format(model_config_path)):
                 suffix = ""
             else:
-                suffix = "_DIR" 
+                suffix = "_DIR"
             if device == "arm":
                 engine.use_lite = self.use_lite
                 engine.use_xpu = self.use_xpu
             if device == "cpu":
-		if use_encryption_model:
+                if use_encryption_model:
                     engine.type = "FLUID_CPU_ANALYSIS_ENCRPT"
                 else:
-                    engine.type = "FLUID_CPU_ANALYSIS"+suffix
+                    engine.type = "FLUID_CPU_ANALYSIS" + suffix
             elif device == "gpu":
-		if use_encryption_model:
+                if use_encryption_model:
                     engine.type = "FLUID_GPU_ANALYSIS_ENCRPT"
                 else:
-                    engine.type = "FLUID_GPU_ANALYSIS"+suffix
+                    engine.type = "FLUID_GPU_ANALYSIS" + suffix
             elif device == "arm":
                 engine.type = "FLUID_ARM_ANALYSIS" + suffix
             self.model_toolkit_conf.engines.extend([engine])
@@ -496,7 +496,7 @@ class Server(object):
                        workdir=None,
                        port=9292,
                        device="cpu",
-		       use_encryption_model=False,
+                       use_encryption_model=False,
                        cube_conf=None):
         if workdir == None:
             workdir = "./tmp"

python/paddle_serving_server_gpu/web_service.py

@@ -295,7 +295,9 @@ class WebService(object):
             feed_dict[var_name] = []
         for feed_ins in feed:
             for key in feed_ins:
-                feed_dict[key].append(np.array(feed_ins[key]).reshape(list(self.feed_vars[key].shape))[np.newaxis,:])
+                feed_dict[key].append(
+                    np.array(feed_ins[key]).reshape(
+                        list(self.feed_vars[key].shape))[np.newaxis, :])
         feed = {}
         for key in feed_dict:
             feed[key] = np.concatenate(feed_dict[key], axis=0)