Merge pull request #900 from wangjiawei04/imagenet_pipeline

Imagenet pipeline

python/examples/imagenet/resnet50_rpc_client.py

@@ -38,7 +38,8 @@ start = time.time()
 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"])
+    fetch_map = client.predict(
+        feed={"image": img}, fetch=["score"], batch=False)
     prob = max(fetch_map["score"][0])
     label = label_dict[fetch_map["score"][0].tolist().index(prob)].strip(
     ).replace(",", "")

python/examples/imagenet/resnet50_web_service.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 import sys
 from paddle_serving_client import Client
-
+import numpy as np
 from paddle_serving_app.reader import Sequential, URL2Image, Resize, CenterCrop, RGB2BGR, Transpose, Div, Normalize, Base64ToImage
 
 if len(sys.argv) != 4:
@@ -44,12 +44,13 @@ class ImageService(WebService):
 
     def preprocess(self, feed=[], fetch=[]):
         feed_batch = []
+        is_batch = True
         for ins in feed:
             if "image" not in ins:
                 raise ("feed data error!")
             img = self.seq(ins["image"])
             feed_batch.append({"image": img[np.newaxis, :]})
-        return feed_batch, fetch
+        return feed_batch, fetch, is_batch
 
     def postprocess(self, feed=[], fetch=[], fetch_map={}):
         score_list = fetch_map["score"]

python/examples/lac/lac_web_service.py

@@ -15,6 +15,7 @@
 from paddle_serving_server.web_service import WebService
 import sys
 from paddle_serving_app.reader import LACReader
+import numpy as np
 
 
 class LACService(WebService):
@@ -23,13 +24,21 @@ class LACService(WebService):
 
     def preprocess(self, feed={}, fetch=[]):
         feed_batch = []
+        fetch = ["crf_decode"]
+        lod_info = [0]
+        is_batch = True
         for ins in feed:
             if "words" not in ins:
                 raise ("feed data error!")
             feed_data = self.reader.process(ins["words"])
-            feed_batch.append({"words": feed_data})
-        fetch = ["crf_decode"]
-        return feed_batch, fetch
+            feed_batch.append(np.array(feed_data).reshape(len(feed_data), 1))
+            lod_info.append(lod_info[-1] + len(feed_data))
+        feed_dict = {
+            "words": np.concatenate(
+                feed_batch, axis=0),
+            "words.lod": lod_info
+        }
+        return feed_dict, fetch, is_batch
 
     def postprocess(self, feed={}, fetch=[], fetch_map={}):
         batch_ret = []

python/examples/pipeline/imagenet/README.md

+# Imagenet Pipeline WebService
+
+This document will takes Imagenet service as an example to introduce how to use Pipeline WebService.
+
+## Get model
+```
+sh get_model.sh
+```
+
+## Start server
+
+```
+python resnet50_web_service.py &>log.txt &
+```
+
+## RPC test
+```
+python pipeline_rpc_client.py
+```

python/examples/pipeline/imagenet/README_CN.md

+# Imagenet Pipeline WebService
+
+这里以 Uci 服务为例来介绍 Pipeline WebService 的使用。
+
+## 获取模型
+```
+sh get_data.sh
+```
+
+## 启动服务
+
+```
+python web_service.py &>log.txt &
+```
+
+## 测试
+```
+curl -X POST -k http://localhost:18082/uci/prediction -d '{"key": ["x"], "value": ["0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727, -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795, -0.0332"]}'
+```

python/examples/pipeline/imagenet/config.yml

+#worker_num, 最大并发数。当build_dag_each_worker=True时, 框架会创建worker_num个进程，每个进程内构建grpcSever和DAG
+##当build_dag_each_worker=False时，框架会设置主线程grpc线程池的max_workers=worker_num
+worker_num: 1
+
+#http端口, rpc_port和http_port不允许同时为空。当rpc_port可用且http_port为空时，不自动生成http_port
+http_port: 18082
+rpc_port: 9999
+
+dag:
+    #op资源类型, True, 为线程模型；False，为进程模型
+    is_thread_op: False
+op:
+    imagenet:
+        #当op配置没有server_endpoints时，从local_service_conf读取本地服务配置
+        local_service_conf:
+
+            #并发数，is_thread_op=True时，为线程并发；否则为进程并发
+            concurrency: 2
+
+            #uci模型路径
+            model_config: ResNet50_vd_model
+
+            #计算硬件ID，当devices为""或不写时为CPU预测；当devices为"0", "0,1,2"时为GPU预测，表示使用的GPU卡
+            devices: "0" # "0,1"
+
+            #client类型，包括brpc, grpc和local_predictor.local_predictor不启动Serving服务，进程内预测
+            client_type: local_predictor
+
+            #Fetch结果列表，以client_config中fetch_var的alias_name为准
+            fetch_list: ["score"] 

python/examples/pipeline/imagenet/get_model.sh

+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/imagenet-example/ResNet50_vd.tar.gz
+tar -xzvf ResNet50_vd.tar.gz
+
+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/imagenet-example/image_data.tar.gz
+tar -xzvf image_data.tar.gz

python/examples/pipeline/imagenet/pipeline_rpc_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.
+from paddle_serving_server_gpu.pipeline import PipelineClient
+import numpy as np
+import requests
+import json
+import cv2
+import base64
+import os
+
+client = PipelineClient()
+client.connect(['127.0.0.1:9999'])
+
+
+def cv2_to_base64(image):
+    return base64.b64encode(image).decode('utf8')
+
+
+with open("daisy.jpg", 'rb') as file:
+    image_data = file.read()
+image = cv2_to_base64(image_data)
+
+for i in range(1):
+    ret = client.predict(feed_dict={"image": image}, fetch=["label", "prob"])
+    print(ret)

python/examples/pipeline/imagenet/resnet50_web_service.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
+from paddle_serving_app.reader import Sequential, URL2Image, Resize, CenterCrop, RGB2BGR, Transpose, Div, Normalize, Base64ToImage
+try:
+    from paddle_serving_server_gpu.web_service import WebService, Op
+except ImportError:
+    from paddle_serving_server.web_service import WebService, Op
+import logging
+import numpy as np
+import base64, cv2
+
+
+class ImagenetOp(Op):
+    def init_op(self):
+        self.seq = Sequential([
+            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, input_dicts, data_id, log_id):
+        (_, input_dict), = input_dicts.items()
+        data = base64.b64decode(input_dict["image"].encode('utf8'))
+        data = np.fromstring(data, np.uint8)
+        # Note: class variables(self.var) can only be used in process op mode
+        im = cv2.imdecode(data, cv2.IMREAD_COLOR)
+        img = self.seq(im)
+        return {"image": img[np.newaxis, :].copy()}, False, None, ""
+
+    def postprocess(self, input_dicts, fetch_dict, log_id):
+        print(fetch_dict)
+        score_list = fetch_dict["score"]
+        result = {"label": [], "prob": []}
+        for score in score_list:
+            score = score.tolist()
+            max_score = max(score)
+            result["label"].append(self.label_dict[score.index(max_score)]
+                                   .strip().replace(",", ""))
+            result["prob"].append(max_score)
+        result["label"] = str(result["label"])
+        result["prob"] = str(result["prob"])
+        return result, None, ""
+
+
+class ImageService(WebService):
+    def get_pipeline_response(self, read_op):
+        image_op = ImagenetOp(name="imagenet", input_ops=[read_op])
+        return image_op
+
+
+uci_service = ImageService(name="imagenet")
+uci_service.prepare_pipeline_config("config.yml")
+uci_service.run_service()

python/examples/senta/senta_web_service.py

@@ -37,6 +37,7 @@ class SentaService(WebService):
     #定义senta模型预测服务的预处理，调用顺序：lac reader->lac模型预测->预测结果后处理->senta reader
     def preprocess(self, feed=[], fetch=[]):
         feed_batch = []
+        is_batch = True
         words_lod = [0]
         for ins in feed:
             if "words" not in ins:
@@ -64,14 +65,13 @@ class SentaService(WebService):
         return {
             "words": np.concatenate(feed_batch),
             "words.lod": words_lod
-        }, fetch
+        }, fetch, is_batch
 
 
 senta_service = SentaService(name="senta")
 senta_service.load_model_config("senta_bilstm_model")
 senta_service.prepare_server(workdir="workdir")
 senta_service.init_lac_client(
-    lac_port=9300,
-    lac_client_config="lac/lac_model/serving_server_conf.prototxt")
+    lac_port=9300, lac_client_config="lac_model/serving_server_conf.prototxt")
 senta_service.run_rpc_service()
 senta_service.run_web_service()

python/pipeline/operator.py

@@ -1343,7 +1343,7 @@ class ResponseOp(Op):
                                 type(var)))
                         _LOGGER.error("(logid={}) Failed to pack RPC "
                                       "response package: {}".format(
-                                          channeldata.id, resp.error_info))
+                                          channeldata.id, resp.err_msg))
                         break
                     resp.value.append(var)
                     resp.key.append(name)

python/pipeline/pipeline_client.py

@@ -23,7 +23,7 @@ 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__)
 
 
@@ -53,7 +53,10 @@ class PipelineClient(object):
         if logid is None:
             req.logid = 0
         else:
-            req.logid = long(logid)
+            if six.PY2:
+                req.logid = long(logid)
+            elif six.PY3:
+                req.logid = int(log_id)
             feed_dict.pop("logid")
 
         clientip = feed_dict.get("clientip")