Revert "add faster rcnn"

This reverts commit 9ba7b510.

python/examples/pipeline/faster_rcnn/benchmark.py

-import sys
-import os
-import yaml
-import requests
-import time
-import json
-import cv2
-import base64
-try:
-    from paddle_serving_server_gpu.pipeline import PipelineClient
-except ImportError:
-    from paddle_serving_server.pipeline import PipelineClient
-import numpy as np
-from paddle_serving_client.utils import MultiThreadRunner
-from paddle_serving_client.utils import benchmark_args, show_latency
-
-def cv2_to_base64(image):
-    return base64.b64encode(image).decode('utf8')
-
-def parse_benchmark(filein, fileout):
-    with open(filein, "r") as fin:
-        res = yaml.load(fin)
-        del_list = []
-        for key in res["DAG"].keys():
-            if "call" in key:
-                del_list.append(key)
-        for key in del_list:
-            del res["DAG"][key]
-    with open(fileout, "w") as fout:
-        yaml.dump(res, fout, default_flow_style=False)
-
-def gen_yml(device):
-    fin = open("config.yml", "r")
-    config = yaml.load(fin)
-    fin.close()
-    config["dag"]["tracer"] = {"interval_s": 10}
-    if device == "gpu":
-        config["op"]["bert"]["local_service_conf"]["device_type"] = 1
-        config["op"]["bert"]["local_service_conf"]["devices"] = "2"        
-    with open("config2.yml", "w") as fout: 
-        yaml.dump(config, fout, default_flow_style=False)
-
-def run_http(idx, batch_size):
-    print("start thread ({})".format(idx))
-    url = "http://127.0.0.1:18082/faster_rcnn/prediction"
-    with open(os.path.join(".", "000000570688.jpg"), 'rb') as file:
-        image_data1 = file.read()
-    image = cv2_to_base64(image_data1)
-
-    start = time.time()
-    for i in range(10):
-        data = {"key": [], "value": []}
-        for j in range(batch_size):
-            data["key"].append("image_" + str(j))
-            data["value"].append(image)
-        r = requests.post(url=url, data=json.dumps(data))
-        print("done")
-    end = time.time()
-    return [[end - start]]
-
-def multithread_http(thread, batch_size):
-    multi_thread_runner = MultiThreadRunner()
-    result = multi_thread_runner.run(run_http , thread, batch_size)
-
-def run_rpc(thread, batch_size):
-    client = PipelineClient()
-    client.connect(['127.0.0.1:9998'])
-    with open("data-c.txt", 'r') as fin:
-        start = time.time()
-        lines = fin.readlines()
-        start_idx = 0
-        while start_idx < len(lines):
-            end_idx = min(len(lines), start_idx + batch_size)
-            feed = {}
-            for i in range(start_idx, end_idx):
-                feed[str(i - start_idx)] = lines[i]
-            ret = client.predict(feed_dict=feed, fetch=["res"])
-            start_idx += batch_size
-            if start_idx > 1000:
-                break
-        end = time.time()
-    return [[end - start]]
-
-
-def multithread_rpc(thraed, batch_size):
-    multi_thread_runner = MultiThreadRunner()
-    result = multi_thread_runner.run(run_rpc , thread, batch_size)
-
-if __name__ == "__main__":
-    if sys.argv[1] == "yaml":
-        mode = sys.argv[2] # brpc/  local predictor
-        thread = int(sys.argv[3])
-        device = sys.argv[4]
-        gen_yml(device)
-    elif sys.argv[1] == "run":
-        mode = sys.argv[2] # http/ rpc
-        thread = int(sys.argv[3])
-        batch_size = int(sys.argv[4])
-        if mode == "http":
-            multithread_http(thread, batch_size)
-        elif mode == "rpc":
-            multithread_rpc(thread, batch_size)
-    elif sys.argv[1] == "dump":
-        filein = sys.argv[2]
-        fileout = sys.argv[3]
-        parse_benchmark(filein, fileout)
-    

python/examples/pipeline/faster_rcnn/benchmark.sh

-export FLAGS_profile_pipeline=1
-alias python3="python3.6"
-modelname="bert"
-# HTTP
-ps -ef | grep web_service | awk '{print $2}' | xargs kill -9 
-sleep 3
-python3 benchmark.py yaml local_predictor 1 cpu 
-rm -rf profile_log_$modelname
-for thread_num in 1  
-do
-  for batch_size in 1 2
-  do
-    echo "----FasterRCNN thread num: $thread_num batch size: $batch_size mode:http ----" >>profile_log_$modelname
-    rm -rf PipelineServingLogs
-    rm -rf cpu_utilization.py
-    python3 web_service.py >web.log 2>&1 &
-    sleep 3
-    nvidia-smi --id=2 --query-compute-apps=used_memory --format=csv -lms 100 > gpu_use.log 2>&1 &
-    nvidia-smi --id=2 --query-gpu=utilization.gpu --format=csv -lms 100 > gpu_utilization.log 2>&1 &
-    echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
-    python3 benchmark.py run http $thread_num $batch_size
-    python3 cpu_utilization.py >>profile_log_$modelname
-    ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
-    python3 benchmark.py dump benchmark.log benchmark.tmp
-    mv benchmark.tmp benchmark.log
-    awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "MAX_GPU_MEMORY:", max}' gpu_use.log >> profile_log_$modelname
-    awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "GPU_UTILIZATION:", max}' gpu_utilization.log >> profile_log_$modelname
-    cat benchmark.log >> profile_log_$modelname
-    #rm -rf gpu_use.log gpu_utilization.log
-  done
-done
-# RPC
-exit
-ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
-sleep 3
-python3 benchmark.py yaml local_predictor 1 gpu
-
-for thread_num in 1 8 16
-do
-  for batch_size in 1 10 100
-  do
-    echo "----Bert thread num: $thread_num batch size: $batch_size mode:rpc ----" >>profile_log_$modelname
-    rm -rf PipelineServingLogs
-    rm -rf cpu_utilization.py
-    python3 web_service.py >web.log 2>&1 &
-    sleep 3
-    nvidia-smi --id=2 --query-compute-apps=used_memory --format=csv -lms 100 > gpu_use.log 2>&1 &
-    nvidia-smi --id=2 --query-gpu=utilization.gpu --format=csv -lms 100 > gpu_utilization.log 2>&1 &
-    echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
-    python3 benchmark.py run rpc $thread_num $batch_size
-    python3 cpu_utilization.py >>profile_log_$modelname
-    ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
-    python3 benchmark.py dump benchmark.log benchmark.tmp
-    mv benchmark.tmp benchmark.log
-    awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "MAX_GPU_MEMORY:", max}' gpu_use.log >> profile_log_$modelname
-    awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "GPU_UTILIZATION:", max}' gpu_utilization.log >> profile_log_$modelname
-    #rm -rf gpu_use.log gpu_utilization.log
-    cat benchmark.log >> profile_log_$modelname
-  done
-done

python/examples/pipeline/faster_rcnn/config.yml

-dag:
-  is_thread_op: false
-  tracer:
-    interval_s: 10
-http_port: 18082
-op:
-  faster_rcnn:
-    local_service_conf:
-      client_type: local_predictor
-      concurrency: 2
-      device_type: 1
-      devices: '2'
-      fetch_list:
-      - save_infer_model/scale_0.tmp_1
-      model_config: serving_server/
-rpc_port: 9998
-worker_num: 20

python/examples/pipeline/faster_rcnn/label_list.txt

-person
-bicycle
-car
-motorcycle
-airplane
-bus
-train
-truck
-boat
-traffic light
-fire hydrant
-stop sign
-parking meter
-bench
-bird
-cat
-dog
-horse
-sheep
-cow
-elephant
-bear
-zebra
-giraffe
-backpack
-umbrella
-handbag
-tie
-suitcase
-frisbee
-skis
-snowboard
-sports ball
-kite
-baseball bat
-baseball glove
-skateboard
-surfboard
-tennis racket
-bottle
-wine glass
-cup
-fork
-knife
-spoon
-bowl
-banana
-apple
-sandwich
-orange
-broccoli
-carrot
-hot dog
-pizza
-donut
-cake
-chair
-couch
-potted plant
-bed
-dining table
-toilet
-tv
-laptop
-mouse
-remote
-keyboard
-cell phone
-microwave
-oven
-toaster
-sink
-refrigerator
-book
-clock
-vase
-scissors
-teddy bear
-hair drier
-toothbrush

python/examples/pipeline/faster_rcnn/pipeline_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.
-# from paddle_serving_server.pipeline import PipelineClient
-import numpy as np
-import requests
-import json
-import cv2
-import base64
-import os
-
-
-def cv2_to_base64(image):
-    return base64.b64encode(image).decode('utf8')
-
-
-url = "http://127.0.0.1:18082/faster_rcnn/prediction"
-with open(os.path.join(".", "000000570688.jpg"), 'rb') as file:
-    image_data1 = file.read()
-image = cv2_to_base64(image_data1)
-
-for i in range(1):
-    data = {"key": ["image"], "value": [image]}
-    r = requests.post(url=url, data=json.dumps(data))
-    print(r.json())

python/examples/pipeline/faster_rcnn/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.
-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 sys
-import cv2
-from paddle_serving_app.reader import *
-import base64
-
-class FasterRCNNOp(Op):
-    def init_op(self):
-        self.img_preprocess = Sequential([
-            BGR2RGB(), Div(255.0),
-            Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], False),
-            Resize((640, 640)), Transpose((2, 0, 1))
-        ])
-        self.img_postprocess = RCNNPostprocess("label_list.txt", "output")
-
-    def preprocess(self, input_dicts, data_id, log_id):
-        (_, input_dict), = input_dicts.items()
-        imgs = []
-        print("keys", input_dict.keys())
-        for key in input_dict.keys():
-            data = base64.b64decode(input_dict[key].encode('utf8'))
-            data = np.fromstring(data, np.uint8)
-            im = cv2.imdecode(data, cv2.IMREAD_COLOR)
-            im = self.img_preprocess(im)
-            imgs.append({
-              "image": im[np.newaxis,:],
-              "im_shape": np.array(list(im.shape[1:])).reshape(-1)[np.newaxis,:],
-              "scale_factor": np.array([1.0, 1.0]).reshape(-1)[np.newaxis,:],
-            })
-        feed_dict = {
-            "image": np.concatenate([x["image"] for x in imgs], axis=0),
-            "im_shape": np.concatenate([x["im_shape"] for x in imgs], axis=0),
-            "scale_factor": np.concatenate([x["scale_factor"] for x in imgs], axis=0)
-        }
-        for key in feed_dict.keys():
-            print(key, feed_dict[key].shape)
-        return feed_dict, False, None, ""
-
-    def postprocess(self, input_dicts, fetch_dict, log_id):
-        #print(fetch_dict)
-        res_dict = {"bbox_result": str(self.img_postprocess(fetch_dict))}
-        return res_dict, None, ""
-
-
-class FasterRCNNService(WebService):
-    def get_pipeline_response(self, read_op):
-        faster_rcnn_op = FasterRCNNOp(name="faster_rcnn", input_ops=[read_op])
-        return faster_rcnn_op
-
-
-fasterrcnn_service = FasterRCNNService(name="faster_rcnn")
-fasterrcnn_service.prepare_pipeline_config("config2.yml")
-fasterrcnn_service.run_service()