Merge branch 'develop' into encryption

python/examples/cascade_rcnn/test_client.py

@@ -33,7 +33,7 @@ fetch_map = client.predict(
         "im_shape": np.array(list(im.shape[1:]) + [1.0])
     },
     fetch=["multiclass_nms_0.tmp_0"],
-    batch=True)
+    batch=False)
 fetch_map["image"] = '000000570688.jpg'
 print(fetch_map)
 postprocess(fetch_map)

python/examples/pipeline/imdb_model_ensemble/README.md

+# IMDB model ensemble examples
+
+## Get models
+```
+sh get_data.sh
+```
+
+## Start servers
+
+```
+python -m paddle_serving_server.serve --model imdb_cnn_model --port 9292 &> cnn.log &
+python -m paddle_serving_server.serve --model imdb_bow_model --port 9393 &> bow.log &
+python test_pipeline_server.py &>pipeline.log &
+```
+
+## Start clients
+```
+python test_pipeline_client.py
+```

python/examples/pipeline/simple_web_service/web_service.py

@@ -17,6 +17,7 @@ except ImportError:
     from paddle_serving_server.web_service import WebService, Op
 import logging
 import numpy as np
+import sys
 
 _LOGGER = logging.getLogger()
 
@@ -31,11 +32,18 @@ class UciOp(Op):
             log_id, input_dict))
         x_value = input_dict["x"]
         proc_dict = {}
-        if isinstance(x_value, (str, unicode)):
-            input_dict["x"] = np.array(
-                [float(x.strip())
-                 for x in x_value.split(self.separator)]).reshape(1, 13)
-            _LOGGER.error("input_dict:{}".format(input_dict))
+        if sys.version_info.major == 2:
+            if isinstance(x_value, (str, unicode)):
+                input_dict["x"] = np.array(
+                    [float(x.strip())
+                     for x in x_value.split(self.separator)]).reshape(1, 13)
+                _LOGGER.error("input_dict:{}".format(input_dict))
+        else:
+            if isinstance(x_value, str):
+                input_dict["x"] = np.array(
+                    [float(x.strip())
+                     for x in x_value.split(self.separator)]).reshape(1, 13)
+                _LOGGER.error("input_dict:{}".format(input_dict))
 
         return input_dict, False, None, ""
 

python/pipeline/analyse.py

@@ -312,7 +312,7 @@ class OpAnalyst(object):
 
         # reduce op times
         op_times = {
-            op_name: sum(step_times.values())
+            op_name: sum(list(step_times.values()))
             for op_name, step_times in op_times.items()
         }
 

python/pipeline/channel.py

@@ -429,9 +429,12 @@ class ProcessChannel(object):
                             self._cv.wait()
                 if self._stop.value == 1:
                     raise ChannelStopError()
-            _LOGGER.debug(
-                self._log("(data_id={} log_id={}) Op({}) Got data".format(
-                    resp.values()[0].id, resp.values()[0].log_id, op_name)))
+
+            if resp is not None:
+                list_values = list(resp.values())
+                _LOGGER.debug(
+                    self._log("(data_id={} log_id={}) Op({}) Got data".format(
+                        list_values[0].id, list_values[0].log_id, op_name)))
             return resp
         elif op_name is None:
             _LOGGER.critical(
@@ -458,11 +461,12 @@ class ProcessChannel(object):
                 try:
                     channeldata = self._que.get(timeout=0)
                     self._output_buf.append(channeldata)
+                    list_values = list(channeldata.values())
                     _LOGGER.debug(
                         self._log(
                             "(data_id={} log_id={}) Op({}) Pop ready item into output_buffer".
-                            format(channeldata.values()[0].id,
-                                   channeldata.values()[0].log_id, op_name)))
+                            format(list_values[0].id, list_values[0].log_id,
+                                   op_name)))
                     break
                 except Queue.Empty:
                     if timeout is not None:
@@ -513,10 +517,12 @@ class ProcessChannel(object):
 
             self._cv.notify_all()
 
-        _LOGGER.debug(
-            self._log(
-                "(data_id={} log_id={}) Op({}) Got data from output_buffer".
-                format(resp.values()[0].id, resp.values()[0].log_id, op_name)))
+        if resp is not None:
+            list_values = list(resp.values())
+            _LOGGER.debug(
+                self._log(
+                    "(data_id={} log_id={}) Op({}) Got data from output_buffer".
+                    format(list_values[0].id, list_values[0].log_id, op_name)))
         return resp
 
     def stop(self):
@@ -726,9 +732,11 @@ class ThreadChannel(Queue.PriorityQueue):
                             self._cv.wait()
                 if self._stop:
                     raise ChannelStopError()
-            _LOGGER.debug(
-                self._log("(data_id={} log_id={}) Op({}) Got data".format(
-                    resp.values()[0].id, resp.values()[0].log_id, op_name)))
+            if resp is not None:
+                list_values = list(resp.values())
+                _LOGGER.debug(
+                    self._log("(data_id={} log_id={}) Op({}) Got data".format(
+                        list_values[0].id, list_values[0].log_id, op_name)))
             return resp
         elif op_name is None:
             _LOGGER.critical(
@@ -755,11 +763,12 @@ class ThreadChannel(Queue.PriorityQueue):
                 try:
                     channeldata = self.get(timeout=0)
                     self._output_buf.append(channeldata)
+                    list_values = list(channeldata.values())
                     _LOGGER.debug(
                         self._log(
                             "(data_id={} log_id={}) Op({}) Pop ready item into output_buffer".
-                            format(channeldata.values()[0].id,
-                                   channeldata.values()[0].log_id, op_name)))
+                            format(list_values[0].id, list_values[0].log_id,
+                                   op_name)))
                     break
                 except Queue.Empty:
                     if timeout is not None:
@@ -810,10 +819,12 @@ class ThreadChannel(Queue.PriorityQueue):
 
             self._cv.notify_all()
 
-        _LOGGER.debug(
-            self._log(
-                "(data_id={} log_id={}) Op({}) Got data from output_buffer".
-                format(resp.values()[0].id, resp.values()[0].log_id, op_name)))
+        if resp is not None:
+            list_values = list(resp.values())
+            _LOGGER.debug(
+                self._log(
+                    "(data_id={} log_id={}) Op({}) Got data from output_buffer".
+                    format(list_values[0].id, list_values[0].log_id, op_name)))
         return resp
 
     def stop(self):

python/pipeline/local_service_handler.py

@@ -105,18 +105,35 @@ class LocalServiceHandler(object):
     def get_port_list(self):
         return self._port_list
 
-    def get_client(self):
+    def get_client(self, concurrency_idx):
         """
         Function get_client is only used for local predictor case, creates one
         LocalPredictor object, and initializes the paddle predictor by function
-        load_model_config.
+        load_model_config.The concurrency_idx is used to select running devices.  
 
         Args:
-            None
+            concurrency_idx: process/thread index
 
         Returns:
             _local_predictor_client
         """
+
+        #checking the legality of concurrency_idx.
+        device_num = len(self._devices)
+        if device_num <= 0:
+            _LOGGER.error("device_num must be not greater than 0. devices({})".
+                          format(self._devices))
+            raise ValueError("The number of self._devices error")
+
+        if concurrency_idx < 0:
+            _LOGGER.error("concurrency_idx({}) must be one positive number".
+                          format(concurrency_idx))
+            concurrency_idx = 0
+        elif concurrency_idx >= device_num:
+            concurrency_idx = concurrency_idx % device_num
+
+        _LOGGER.info("GET_CLIENT : concurrency_idx={}, device_num={}".format(
+            concurrency_idx, device_num))
         from paddle_serving_app.local_predict import LocalPredictor
         if self._local_predictor_client is None:
             self._local_predictor_client = LocalPredictor()
@@ -126,7 +143,7 @@ class LocalServiceHandler(object):
             self._local_predictor_client.load_model_config(
                 model_path=self._model_config,
                 use_gpu=use_gpu,
-                gpu_id=self._devices[0],
+                gpu_id=self._devices[concurrency_idx],
                 use_profile=self._use_profile,
                 thread_num=self._thread_num,
                 mem_optim=self._mem_optim,

python/pipeline/operator.py

@@ -55,7 +55,7 @@ class Op(object):
                  client_type=None,
                  concurrency=None,
                  timeout=None,
-                 retry=None,
+                 retry=0,
                  batch_size=None,
                  auto_batching_timeout=None,
                  local_service_handler=None):
@@ -574,7 +574,7 @@ class Op(object):
         #Init cuda env in main thread
         if self.client_type == "local_predictor":
             _LOGGER.info("Init cuda env in main thread")
-            self.local_predictor = self._local_service_handler.get_client()
+            self.local_predictor = self._local_service_handler.get_client(0)
 
         threads = []
         for concurrency_idx in range(self.concurrency):
@@ -679,7 +679,7 @@ class Op(object):
         err_channeldata_dict = collections.OrderedDict()
         ### if (batch_num == 1 && skip == True) ,then skip the process stage.
         is_skip_process = False
-        data_ids = preped_data_dict.keys()
+        data_ids = list(preped_data_dict.keys())
         if len(data_ids) == 1 and skip_process_dict.get(data_ids[0]) == True:
             is_skip_process = True
             _LOGGER.info("(data_id={} log_id={}) skip process stage".format(
@@ -1034,7 +1034,8 @@ class Op(object):
 
                 _LOGGER.info("Init cuda env in process {}".format(
                     concurrency_idx))
-                self.local_predictor = self.service_handler.get_client()
+                self.local_predictor = self.service_handler.get_client(
+                    concurrency_idx)
             # check all ops initialized successfully.
             profiler = self._initialize(is_thread_op, concurrency_idx)
 

python/pipeline/pipeline_client.py

@@ -53,10 +53,10 @@ class PipelineClient(object):
         if logid is None:
             req.logid = 0
         else:
-            if six.PY2:
+            if sys.version_info.major == 2:
                 req.logid = long(logid)
-            elif six.PY3:
-                req.logid = int(log_id)
+            elif sys.version_info.major == 3:
+                req.logid = int(logid)
             feed_dict.pop("logid")
 
         clientip = feed_dict.get("clientip")
@@ -71,10 +71,15 @@ class PipelineClient(object):
         np.set_printoptions(threshold=sys.maxsize)
         for key, value in feed_dict.items():
             req.key.append(key)
+
+            if (sys.version_info.major == 2 and isinstance(value,
+                                                           (str, unicode)) or
+                ((sys.version_info.major == 3) and isinstance(value, str))):
+                req.value.append(value)
+                continue
+
             if isinstance(value, np.ndarray):
                 req.value.append(value.__repr__())
-            elif isinstance(value, (str, unicode)):
-                req.value.append(value)
             elif isinstance(value, list):
                 req.value.append(np.array(value).__repr__())
             else: