# 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 grpc import sys import time import numpy as np from numpy import * import logging import functools import json import socket from .channel import ChannelDataErrcode from .proto import pipeline_service_pb2 from .proto import pipeline_service_pb2_grpc import six _LOGGER = logging.getLogger(__name__) class PipelineClient(object): """ PipelineClient provides the basic capabilities of the pipeline SDK """ def __init__(self): self._channel = None self._profile_key = "pipeline.profile" self._profile_value = "1" def connect(self, endpoints): options = [('grpc.max_receive_message_length', 512 * 1024 * 1024), ('grpc.max_send_message_length', 512 * 1024 * 1024), ('grpc.lb_policy_name', 'round_robin')] g_endpoint = 'ipv4:{}'.format(','.join(endpoints)) self._channel = grpc.insecure_channel(g_endpoint, options=options) self._stub = pipeline_service_pb2_grpc.PipelineServiceStub( self._channel) def _pack_request_package(self, feed_dict, pack_tensor_format, profile): req = pipeline_service_pb2.Request() logid = feed_dict.get("logid") if logid is None: req.logid = 0 else: if sys.version_info.major == 2: req.logid = long(logid) elif sys.version_info.major == 3: req.logid = int(logid) feed_dict.pop("logid") clientip = feed_dict.get("clientip") if clientip is None: hostname = socket.gethostname() ip = socket.gethostbyname(hostname) req.clientip = ip else: req.clientip = clientip feed_dict.pop("clientip") np.set_printoptions(threshold=sys.maxsize) if pack_tensor_format is False: # pack string key/val format 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, list): req.value.append(np.array(value).__repr__()) else: raise TypeError( "only str and np.ndarray type is supported: {}".format( type(value))) if profile: req.key.append(self._profile_key) req.value.append(self._profile_value) else: # pack tensor format for key, value in feed_dict.items(): one_tensor = req.tensors.add() one_tensor.name = key if (sys.version_info.major == 2 and isinstance(value, (str, unicode)) or ((sys.version_info.major == 3) and isinstance(value, str))): one_tensor.string_data.add(value) one_tensor.elem_type = 12 #12 => string continue if isinstance(value, np.ndarray): # copy shape _LOGGER.info("value shape is {}".format(value.shape)) for one_dim in value.shape: one_tensor.shape.append(one_dim) flat_value = value.flatten().tolist() # copy data if value.dtype == "int64": one_tensor.int64_data.extend(flat_value) one_tensor.elem_type = 0 elif value.dtype == "float32": one_tensor.float_data.extend(flat_value) one_tensor.elem_type = 1 elif value.dtype == "int32": one_tensor.int_data.extend(flat_value) one_tensor.elem_type = 2 elif value.dtype == "float64": one_tensor.float64_data.extend(flat_value) one_tensor.elem_type = 3 elif value.dtype == "int16": one_tensor.int_data.extend(flat_value) one_tensor.elem_type = 4 elif value.dtype == "float16": one_tensor.float_data.extend(flat_value) one_tensor.elem_type = 5 elif value.dtype == "uint16": one_tensor.uint32_data.extend(flat_value) one_tensor.elem_type = 6 elif value.dtype == "uint8": one_tensor.uint32_data.extend(flat_value) one_tensor.elem_type = 7 elif value.dtype == "int8": one_tensor.int_data.extend(flat_value) one_tensor.elem_type = 8 elif value.dtype == "bool": one_tensor.bool_data.extend(flat_value) one_tensor.elem_type = 9 else: _LOGGER.error( "value type {} of tensor {} is not supported.". format(value.dtype, key)) else: raise TypeError( "only str and np.ndarray type is supported: {}".format( type(value))) return req def _unpack_response_package(self, resp, fetch): return resp def predict(self, feed_dict, fetch=None, asyn=False, pack_tensor_format=False, profile=False, log_id=0): if not isinstance(feed_dict, dict): raise TypeError( "feed must be dict type with format: {name: value}.") if fetch is not None and not isinstance(fetch, list): raise TypeError("fetch must be list type with format: [name].") print("PipelineClient::predict pack_data time:{}".format(time.time())) req = self._pack_request_package(feed_dict, pack_tensor_format, profile) req.logid = log_id if not asyn: print("PipelineClient::predict before time:{}".format(time.time())) resp = self._stub.inference(req) return self._unpack_response_package(resp, fetch) else: call_future = self._stub.inference.future(req) return PipelinePredictFuture( call_future, functools.partial( self._unpack_response_package, fetch=fetch)) class PipelinePredictFuture(object): def __init__(self, call_future, callback_func): self.call_future_ = call_future self.callback_func_ = callback_func def result(self): resp = self.call_future_.result() return self.callback_func_(resp)