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未验证 提交 e9ecf97c 编写于 作者: J Jiawei Wang 提交者： GitHub
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Merge pull request #1249 from bjjwwang/v0.6.0 

V0.6.0 pick clas and auth
上级 7a913d67 8f7f489b
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doc/PADDLE_SERVING_ON_KUBERNETES.md
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...@@ -4,9 +4,9 @@ Paddle Serving在0.6.0版本开始支持在Kubenetes集群上部署，并提供 ...@@ -4,9 +4,9 @@ Paddle Serving在0.6.0版本开始支持在Kubenetes集群上部署，并提供
### 集群准备 ### 集群准备
如果您还没有Kubenetes集群，我们推荐[购买并使用百度智能云CCE集群](). 如果是其他云服务商提供的集群，或者自行安装Kubenetes集群，请遵照对应的教程。 如果您还没有Kubenetes集群，我们推荐[购买并使用百度智能云CCE集群](https://cloud.baidu.com/doc/CCE/index.html). 如果是其他云服务商提供的集群，或者自行安装Kubenetes集群，请遵照对应的教程。
您还需要准备一个用于Kubenetes集群部署使用的镜像仓库，通常与云服务提供商绑定，如果您使用的是百度智能云的CCE集群，可以参照[百度智能云CCR镜像仓库使用方式]()。当然Docker Hub也可以作为镜像仓库，但是可能在部署时会出现下载速度慢的情况。 您还需要准备一个用于Kubenetes集群部署使用的镜像仓库，通常与云服务提供商绑定，如果您使用的是百度智能云的CCE集群，可以参照[百度智能云CCR镜像仓库使用方式](https://cloud.baidu.com/doc/CCR/index.html)。当然Docker Hub也可以作为镜像仓库，但是可能在部署时会出现下载速度慢的情况。
### 环境准备 ### 环境准备
......
doc/SERVING_AUTH_DOCKER.md 0 → 100644
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# 在Paddle Serving使用安全网关
## 简介
在之前的服务部署示例中，我们都从开发的角度切入，然而，在现实的生产环境中，仅仅提供一个能够预测的远端服务接口还远远不够。我们仍然要考虑以下不足。
- 这个服务还不能以网关的形式提供，访问路径难以管理。
- 这个服务接口不够安全，需要做相应的鉴权。
- 这个服务接口不能够控制流量，无法合理利用资源。
本文档的作用，就以 Uci 房价预测服务为例，来介绍如何强化预测服务API接口安全。API网关作为流量入口，对接口进行统一管理。但API网关可以提供流量加密和鉴权等安全功能。
## Docker部署
可以使用docker-compose来部署安全网关。这个示例的步骤就是 [部署本地Serving容器] - [部署本地安全网关] - [通过安全网关访问Serving]
**注明：** docker-compose与docker不一样，它依赖于docker，一次可以部署多个docker容器，可以类比于本地版的kubenetes，docker-compose的教程请参考[docker-compose安装](https://docs.docker.com/compose/install/)
```shell
docker-compose -f tools/auth/auth-serving-docker.yaml up -d
```
可以通过 `docker ps` 来查看启动的容器。
```shell
3035cf445029 pantsel/konga:next "/app/start.sh" About an hour ago Up About an hour 0.0.0.0:8005->1337/tcp anquan_konga_1
7ce3abee550c registry.baidubce.com/serving_gateway/kong:paddle "/docker-entrypoint.…" About an hour ago Up About an hour (healthy) 0.0.0.0:8000->8000/tcp, 127.0.0.1:8001->8001/tcp, 0.0.0.0:8443->8443/tcp, 127.0.0.1:8444->8444/tcp anquan_kong_1
25810fd79a27 postgres:9.6 "docker-entrypoint.s…" About an hour ago Up About an hour (healthy) 5432/tcp anquan_db_1
ee59a3dd4806 registry.baidubce.com/serving_dev/serving-runtime:cpu-py36 "bash -c ' wget --no…" About an hour ago Up About an hour 0.0.0.0:9393->9393/tcp anquan_serving_1
665fd8a34e15 redis:latest "docker-entrypoint.s…" About an hour ago Up About an hour 0.0.0.0:6379->6379/tcp anquan_redis_1
```
其中我们之前serving容器 以 9393端口暴露，KONG网关的端口是8443， KONG的Web控制台的端口是8001。接下来我们在浏览器访问 `https://$IP_ADDR:8001`, 其中 IP_ADDR就是宿主机的IP。
<img src="kong-dashboard.png">
可以看到在注册结束后，登陆，看到了 DASHBOARD，我们先看SERVICES，可以看到`serving_service`，这意味着我们端口在9393的Serving服务已经在KONG当中被注册。
<img src="kong-services.png">
<img src="kong-routes.png">
然后在ROUTES中，我们可以看到 serving 被链接到了 `/serving-uci`。
最后我们点击 CONSUMERS - default_user - Credentials - API KEYS ，我们可以看到 `Api Keys` 下看到很多key
<img src="kong-api_keys.png">
接下来可以通过curl访问
```shell
curl -H "Content-Type:application/json" -H "X-INSTANCE-ID:kong_ins" -H "apikey:hP6v25BQVS5CcS1nqKpxdrFkUxze9JWD" -X POST -d '{"feed":[{"x": [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]}], "fetch":["price"]}' https://127.0.0.1:8443/serving-uci/uci/prediction -k
```
与之前的Serving HTTP服务相比，有以下区别。
- 使用https加密访问，而不是http
- 使用serving_uci的路径映射到网关
- 在header处增加了 `X-INSTANCE-ID`和`apikey`
## K8S部署
同样，我们也提供了K8S集群部署Serving安全网关的方式。
### Step 1：启动Serving服务
我们仍然以 [Uci房价预测](../python/examples/fit_a_line)服务作为例子，这里省略了镜像制作的过程，详情可以参考 [在Kubernetes集群上部署Paddle Serving](./PADDLE_SERVING_ON_KUBERNETES.md)。
在这里我们直接执行
```
kubectl apply -f tools/auth/serving-demo-k8s.yaml
```
可以看到
### Step 2: 安装 KONG (一个集群只需要执行一次就可以)
接下来我们执行KONG Ingress的安装
```
kubectl apply -f tools/auth/kong-install.yaml
```
输出是
```
namespace/kong created
customresourcedefinition.apiextensions.k8s.io/kongclusterplugins.configuration.konghq.com created
customresourcedefinition.apiextensions.k8s.io/kongconsumers.configuration.konghq.com created
customresourcedefinition.apiextensions.k8s.io/kongingresses.configuration.konghq.com created
customresourcedefinition.apiextensions.k8s.io/kongplugins.configuration.konghq.com created
customresourcedefinition.apiextensions.k8s.io/tcpingresses.configuration.konghq.com created
serviceaccount/kong-serviceaccount created
clusterrole.rbac.authorization.k8s.io/kong-ingress-clusterrole created
clusterrolebinding.rbac.authorization.k8s.io/kong-ingress-clusterrole-nisa-binding created
service/kong-proxy created
service/kong-validation-webhook created
deployment.apps/ingress-kong created
```
我们可以输入
```
kubectl get service --all-namespaces
```
会显示
```
NAMESPACE NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
default uci ClusterIP 172.16.87.89 <none> 9393/TCP 7d7h
kong kong-proxy NodePort 172.16.23.91 <none> 80:8175/TCP,443:8521/TCP 102m
kong kong-validation-webhook ClusterIP 172.16.114.93 <none> 443/TCP 102m
```
### Step 3: 创建Ingress资源
接下来需要做Serving服务和KONG的链接
```
kubectl apply -f tools/auth/kong-ingress-k8s.yaml
```
我们也给出yaml文件内容
```
apiVersion: extensions/v1beta1
kind: Ingress
metadata:
name: demo
annotations:
konghq.com/strip-path: "true"
kubernetes.io/ingress.class: kong
spec:
rules:
- http:
paths:
- path: /foo
backend:
serviceName: {{SERVING_SERVICE_NAME}}
servicePort: {{SERVICE_PORT}}
```
其中serviceName就是uci，servicePort就是9393，如果是别的服务就需要改这两个字段，最终会映射到`/foo`下。
在这一步之后，我们就可以
```
curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"x": [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]}], "fetch":["price"]}' http://$IP:$PORT/foo/uci/prediction
```
### Step 4: 增加安全网关限制
之前的接口没有鉴权功能，无法验证用户身份合法性，现在我们添加一个key-auth插件
执行
```
kubectl apply -f key-auth-k8s.yaml
```
其中,yaml文内容为
```
apiVersion: configuration.konghq.com/v1
kind: KongPlugin
metadata:
name: key-auth
plugin: key-auth
```
现在，需要创建secret，key值为用户指定，需要在请求时携带Header中apikey字段
执行
```
kubectl create secret generic default-apikey \
--from-literal=kongCredType=key-auth \
--from-literal=key=ZGVmYXVsdC1hcGlrZXkK
```
在这里，我们的key是随意制定了一串 `ZGVmYXVsdC1hcGlrZXkK`，实际情况也可以
创建一个用户（consumer）标识访问者身份，并未该用户绑定apikey。
执行
```
kubectl apply -f kong-consumer-k8s.yaml
```
其中,yaml文内容为
```
apiVersion: configuration.konghq.com/v1
kind: KongConsumer
metadata:
name: default
annotations:
kubernetes.io/ingress.class: kong
username: default
credentials:
- default-apikey
```
如果我们这时还想再像上一步一样的做curl访问，会发现已经无法访问，此时已经具备了安全能力，我们需要对应的key。
### Step 5: 通过API Key访问服务
执行
```
curl -H "Content-Type:application/json" -H "apikey:ZGVmYXVsdC1hcGlrZXkK" -X POST -d '{"feed":[{"x": [0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727, -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795, -0.0332]}], "fetch":["price"]}' https://$IP:$PORT/foo/uci/prediction -k
```
我们可以看到 apikey 已经加入到了curl请求的header当中。
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# 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
```
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# Imagenet Pipeline WebService
这里以 Imagenet 服务为例来介绍 Pipeline WebService 的使用。
## 获取模型
```
sh get_model.sh
```
## 启动服务
```
python resnet50_web_service.py &>log.txt &
```
## 测试
```
python pipeline_rpc_client.py
```
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import sys
import os
import base64
import yaml
import requests
import time
import json
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 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, gpu_id):
fin = open("config.yml", "r")
config = yaml.load(fin)
fin.close()
config["dag"]["tracer"] = {"interval_s": 10}
if device == "gpu":
config["op"]["imagenet"]["local_service_conf"]["device_type"] = 1
config["op"]["imagenet"]["local_service_conf"]["devices"] = gpu_id
else:
config["op"]["imagenet"]["local_service_conf"]["device_type"] = 0
with open("config2.yml", "w") as fout:
yaml.dump(config, fout, default_flow_style=False)
def cv2_to_base64(image):
return base64.b64encode(image).decode('utf8')
def run_http(idx, batch_size):
print("start thread ({})".format(idx))
url = "http://127.0.0.1:18080/imagenet/prediction"
start = time.time()
with open(os.path.join(".", "daisy.jpg"), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
keys, values = [], []
for i in range(batch_size):
keys.append("image_{}".format(i))
values.append(image)
data = {"key": keys, "value": values}
latency_list = []
start_time = time.time()
total_num = 0
while True:
l_start = time.time()
r = requests.post(url=url, data=json.dumps(data))
print(r.json())
l_end = time.time()
latency_list.append(l_end * 1000 - l_start * 1000)
total_num += 1
if time.time() - start_time > 20:
break
end = time.time()
return [[end - start], latency_list, [total_num]]
def multithread_http(thread, batch_size):
multi_thread_runner = MultiThreadRunner()
start = time.time()
result = multi_thread_runner.run(run_http, thread, batch_size)
end = time.time()
total_cost = end - start
avg_cost = 0
total_number = 0
for i in range(thread):
avg_cost += result[0][i]
total_number += result[2][i]
avg_cost = avg_cost / thread
print("Total cost: {}s".format(total_cost))
print("Each thread cost: {}s. ".format(avg_cost))
print("Total count: {}. ".format(total_number))
print("AVG QPS: {} samples/s".format(batch_size * total_number /
total_cost))
show_latency(result[1])
def run_rpc(thread, batch_size):
client = PipelineClient()
client.connect(['127.0.0.1:18080'])
start = time.time()
test_img_dir = "imgs/"
for img_file in os.listdir(test_img_dir):
with open(os.path.join(test_img_dir, img_file), 'rb') as file:
image_data = file.read()
image = cv2_to_base64(image_data)
start_time = time.time()
while True:
ret = client.predict(feed_dict={"image": image}, fetch=["res"])
if time.time() - start_time > 10:
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]
if device == "gpu":
gpu_id = sys.argv[5]
else:
gpu_id = None
gen_yml(device, gpu_id)
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)
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export FLAGS_profile_pipeline=1
alias python3="python3.6"
modelname="clas-DarkNet53"
# HTTP
#ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3
# Create yaml，If you already have the config.yaml, ignore it.
#python3 benchmark.py yaml local_predictor 1 gpu
rm -rf profile_log_$modelname
echo "Starting HTTP Clients..."
# Start a client in each thread, tesing the case of multiple threads.
for thread_num in 1 2 4 8 12 16
do
for batch_size in 1
do
echo "----${modelname} thread num: ${thread_num} batch size: ${batch_size} mode:http ----" >>profile_log_$modelname
# Start one web service, If you start the service yourself, you can ignore it here.
#python3 web_service.py >web.log 2>&1 &
#sleep 3
# --id is the serial number of the GPU card, Must be the same as the gpu id used by the server.
nvidia-smi --id=3 --query-gpu=memory.used --format=csv -lms 1000 > gpu_use.log 2>&1 &
nvidia-smi --id=3 --query-gpu=utilization.gpu --format=csv -lms 1000 > gpu_utilization.log 2>&1 &
echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
# Start http client
python3 benchmark.py run http $thread_num $batch_size > profile 2>&1
# Collect CPU metrics, Filter data that is zero momentarily, Record the maximum value of GPU memory and the average value of GPU utilization
python3 cpu_utilization.py >> profile_log_$modelname
grep -av '^0 %' gpu_utilization.log > gpu_utilization.log.tmp
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 -F' ' '{sum+=$1} END {print "GPU_UTILIZATION:", sum/NR, sum, NR }' gpu_utilization.log.tmp >> profile_log_$modelname
# Show profiles
python3 ../../../util/show_profile.py profile $thread_num >> profile_log_$modelname
tail -n 8 profile >> profile_log_$modelname
echo '' >> profile_log_$modelname
done
done
# Kill all nvidia-smi background task.
pkill nvidia-smi
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cuda_version: "10.1"
cudnn_version: "7.6"
trt_version: "6.0"
python_version: "3.7"
gcc_version: "8.2"
paddle_version: "2.0.1"
cpu: "Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz X12"
gpu: "T4"
xpu: "None"
api: ""
owner: "cuicheng01"
model_name: "DarkNet53"
model_type: "static"
model_source: "PaddleClas"
model_url: "https://paddle-imagenet-models-name.bj.bcebos.com/DarkNet53_pretrained.tar"
batch_size: 1
num_of_samples: 1000
input_shape: "3,224,224"
runtime_device: "gpu"
ir_optim: true
enable_memory_optim: true
enable_tensorrt: false
precision: "fp32"
enable_mkldnn: false
cpu_math_library_num_threads: ""
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cuda_version: "10.1"
cudnn_version: "7.6"
trt_version: "6.0"
python_version: "3.7"
gcc_version: "8.2"
paddle_version: "2.0.1"
cpu: "Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz X12"
gpu: "T4"
xpu: "None"
api: ""
owner: "cuicheng01"
model_name: "imagenet"
model_type: "static"
model_source: "PaddleClas"
model_url: "model_url_path"
batch_size: 1
num_of_samples: 1000
input_shape: "3,224,224"
runtime_device: "cpu"
ir_optim: true
enable_memory_optim: true
enable_tensorrt: false
precision: "fp32"
enable_mkldnn: false
cpu_math_library_num_threads: ""
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export FLAGS_profile_pipeline=1
alias python3="python3.7"
modelname="imagenet"
use_gpu=1
gpu_id="0"
benchmark_config_filename="benchmark_config.yaml"
# HTTP
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3
if [ $use_gpu -eq 1 ]; then
python3 benchmark.py yaml local_predictor 1 gpu $gpu_id
else
python3 benchmark.py yaml local_predictor 1 cpu
fi
rm -rf profile_log_$modelname
for thread_num in 1
do
for batch_size in 1
do
echo "#----imagenet thread num: $thread_num batch size: $batch_size mode:http use_gpu:$use_gpu----" >>profile_log_$modelname
rm -rf PipelineServingLogs
rm -rf cpu_utilization.py
python3 resnet50_web_service.py >web.log 2>&1 &
sleep 3
nvidia-smi --id=${gpu_id} --query-compute-apps=used_memory --format=csv -lms 100 > gpu_use.log 2>&1 &
nvidia-smi --id=${gpu_id} --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
python3 -m paddle_serving_server_gpu.profiler >>profile_log_$modelname
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
ps -ef | grep nvidia-smi | 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=$modelname}} END {print "GPU_MEM:", max}' gpu_use.log >> profile_log_$modelname
awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "GPU_UTIL:", max}' gpu_utilization.log >> profile_log_$modelname
cat benchmark.log >> profile_log_$modelname
python3 -m paddle_serving_server_gpu.parse_profile --benchmark_cfg $benchmark_config_filename --benchmark_log profile_log_$modelname
#rm -rf gpu_use.log gpu_utilization.log
done
done
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浏览文件 @ e9ecf97c
#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: 18080
rpc_port: 9993
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: 1
#uci模型路径
model_config: DarkNet53/ppcls_model/
#计算硬件类型: 空缺时由devices决定(CPU/GPU)，0=cpu, 1=gpu, 2=tensorRT, 3=arm cpu, 4=kunlun xpu
device_type: 1
#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: ["prediction"]
python/examples/pipeline/PaddleClas/DarkNet53/cpu_utilization.py 0 → 100644
浏览文件 @ e9ecf97c
import psutil
cpu_utilization=psutil.cpu_percent(1,False)
print('CPU_UTILIZATION:', cpu_utilization)
python/examples/pipeline/PaddleClas/DarkNet53/get_model.sh 0 → 100644
浏览文件 @ e9ecf97c
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/model/DarkNet53.tar
tar -xf DarkNet53.tar
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/PaddleClas/DarkNet53/imagenet.label 0 → 100644
浏览文件 @ e9ecf97c
tench, Tinca tinca,
goldfish, Carassius auratus,
great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias,
tiger shark, Galeocerdo cuvieri,
hammerhead, hammerhead shark,
electric ray, crampfish, numbfish, torpedo,
stingray,
cock,
hen,
ostrich, Struthio camelus,
brambling, Fringilla montifringilla,
goldfinch, Carduelis carduelis,
house finch, linnet, Carpodacus mexicanus,
junco, snowbird,
indigo bunting, indigo finch, indigo bird, Passerina cyanea,
robin, American robin, Turdus migratorius,
bulbul,
jay,
magpie,
chickadee,
water ouzel, dipper,
kite,
bald eagle, American eagle, Haliaeetus leucocephalus,
vulture,
great grey owl, great gray owl, Strix nebulosa,
European fire salamander, Salamandra salamandra,
common newt, Triturus vulgaris,
eft,
spotted salamander, Ambystoma maculatum,
axolotl, mud puppy, Ambystoma mexicanum,
bullfrog, Rana catesbeiana,
tree frog, tree-frog,
tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui,
loggerhead, loggerhead turtle, Caretta caretta,
leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea,
mud turtle,
terrapin,
box turtle, box tortoise,
banded gecko,
common iguana, iguana, Iguana iguana,
American chameleon, anole, Anolis carolinensis,
whiptail, whiptail lizard,
agama,
frilled lizard, Chlamydosaurus kingi,
alligator lizard,
Gila monster, Heloderma suspectum,
green lizard, Lacerta viridis,
African chameleon, Chamaeleo chamaeleon,
Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis,
African crocodile, Nile crocodile, Crocodylus niloticus,
American alligator, Alligator mississipiensis,
triceratops,
thunder snake, worm snake, Carphophis amoenus,
ringneck snake, ring-necked snake, ring snake,
hognose snake, puff adder, sand viper,
green snake, grass snake,
king snake, kingsnake,
garter snake, grass snake,
water snake,
vine snake,
night snake, Hypsiglena torquata,
boa constrictor, Constrictor constrictor,
rock python, rock snake, Python sebae,
Indian cobra, Naja naja,
green mamba,
sea snake,
horned viper, cerastes, sand viper, horned asp, Cerastes cornutus,
diamondback, diamondback rattlesnake, Crotalus adamanteus,
sidewinder, horned rattlesnake, Crotalus cerastes,
trilobite,
harvestman, daddy longlegs, Phalangium opilio,
scorpion,
black and gold garden spider, Argiope aurantia,
barn spider, Araneus cavaticus,
garden spider, Aranea diademata,
black widow, Latrodectus mactans,
tarantula,
wolf spider, hunting spider,
tick,
centipede,
black grouse,
ptarmigan,
ruffed grouse, partridge, Bonasa umbellus,
prairie chicken, prairie grouse, prairie fowl,
peacock,
quail,
partridge,
African grey, African gray, Psittacus erithacus,
macaw,
sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita,
lorikeet,
coucal,
bee eater,
hornbill,
hummingbird,
jacamar,
toucan,
drake,
red-breasted merganser, Mergus serrator,
goose,
black swan, Cygnus atratus,
tusker,
echidna, spiny anteater, anteater,
platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus,
wallaby, brush kangaroo,
koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus,
wombat,
jellyfish,
sea anemone, anemone,
brain coral,
flatworm, platyhelminth,
nematode, nematode worm, roundworm,
conch,
snail,
slug,
sea slug, nudibranch,
chiton, coat-of-mail shell, sea cradle, polyplacophore,
chambered nautilus, pearly nautilus, nautilus,
Dungeness crab, Cancer magister,
rock crab, Cancer irroratus,
fiddler crab,
king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica,
American lobster, Northern lobster, Maine lobster, Homarus americanus,
spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish,
crayfish, crawfish, crawdad, crawdaddy,
hermit crab,
isopod,
white stork, Ciconia ciconia,
black stork, Ciconia nigra,
spoonbill,
flamingo,
little blue heron, Egretta caerulea,
American egret, great white heron, Egretta albus,
bittern,
crane,
limpkin, Aramus pictus,
European gallinule, Porphyrio porphyrio,
American coot, marsh hen, mud hen, water hen, Fulica americana,
bustard,
ruddy turnstone, Arenaria interpres,
red-backed sandpiper, dunlin, Erolia alpina,
redshank, Tringa totanus,
dowitcher,
oystercatcher, oyster catcher,
pelican,
king penguin, Aptenodytes patagonica,
albatross, mollymawk,
grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus,
killer whale, killer, orca, grampus, sea wolf, Orcinus orca,
dugong, Dugong dugon,
sea lion,
Chihuahua,
Japanese spaniel,
Maltese dog, Maltese terrier, Maltese,
Pekinese, Pekingese, Peke,
Shih-Tzu,
Blenheim spaniel,
papillon,
toy terrier,
Rhodesian ridgeback,
Afghan hound, Afghan,
basset, basset hound,
beagle,
bloodhound, sleuthhound,
bluetick,
black-and-tan coonhound,
Walker hound, Walker foxhound,
English foxhound,
redbone,
borzoi, Russian wolfhound,
Irish wolfhound,
Italian greyhound,
whippet,
Ibizan hound, Ibizan Podenco,
Norwegian elkhound, elkhound,
otterhound, otter hound,
Saluki, gazelle hound,
Scottish deerhound, deerhound,
Weimaraner,
Staffordshire bullterrier, Staffordshire bull terrier,
American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier,
Bedlington terrier,
Border terrier,
Kerry blue terrier,
Irish terrier,
Norfolk terrier,
Norwich terrier,
Yorkshire terrier,
wire-haired fox terrier,
Lakeland terrier,
Sealyham terrier, Sealyham,
Airedale, Airedale terrier,
cairn, cairn terrier,
Australian terrier,
Dandie Dinmont, Dandie Dinmont terrier,
Boston bull, Boston terrier,
miniature schnauzer,
giant schnauzer,
standard schnauzer,
Scotch terrier, Scottish terrier, Scottie,
Tibetan terrier, chrysanthemum dog,
silky terrier, Sydney silky,
soft-coated wheaten terrier,
West Highland white terrier,
Lhasa, Lhasa apso,
flat-coated retriever,
curly-coated retriever,
golden retriever,
Labrador retriever,
Chesapeake Bay retriever,
German short-haired pointer,
vizsla, Hungarian pointer,
English setter,
Irish setter, red setter,
Gordon setter,
Brittany spaniel,
clumber, clumber spaniel,
English springer, English springer spaniel,
Welsh springer spaniel,
cocker spaniel, English cocker spaniel, cocker,
Sussex spaniel,
Irish water spaniel,
kuvasz,
schipperke,
groenendael,
malinois,
briard,
kelpie,
komondor,
Old English sheepdog, bobtail,
Shetland sheepdog, Shetland sheep dog, Shetland,
collie,
Border collie,
Bouvier des Flandres, Bouviers des Flandres,
Rottweiler,
German shepherd, German shepherd dog, German police dog, alsatian,
Doberman, Doberman pinscher,
miniature pinscher,
Greater Swiss Mountain dog,
Bernese mountain dog,
Appenzeller,
EntleBucher,
boxer,
bull mastiff,
Tibetan mastiff,
French bulldog,
Great Dane,
Saint Bernard, St Bernard,
Eskimo dog, husky,
malamute, malemute, Alaskan malamute,
Siberian husky,
dalmatian, coach dog, carriage dog,
affenpinscher, monkey pinscher, monkey dog,
basenji,
pug, pug-dog,
Leonberg,
Newfoundland, Newfoundland dog,
Great Pyrenees,
Samoyed, Samoyede,
Pomeranian,
chow, chow chow,
keeshond,
Brabancon griffon,
Pembroke, Pembroke Welsh corgi,
Cardigan, Cardigan Welsh corgi,
toy poodle,
miniature poodle,
standard poodle,
Mexican hairless,
timber wolf, grey wolf, gray wolf, Canis lupus,
white wolf, Arctic wolf, Canis lupus tundrarum,
red wolf, maned wolf, Canis rufus, Canis niger,
coyote, prairie wolf, brush wolf, Canis latrans,
dingo, warrigal, warragal, Canis dingo,
dhole, Cuon alpinus,
African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus,
hyena, hyaena,
red fox, Vulpes vulpes,
kit fox, Vulpes macrotis,
Arctic fox, white fox, Alopex lagopus,
grey fox, gray fox, Urocyon cinereoargenteus,
tabby, tabby cat,
tiger cat,
Persian cat,
Siamese cat, Siamese,
Egyptian cat,
cougar, puma, catamount, mountain lion, painter, panther, Felis concolor,
lynx, catamount,
leopard, Panthera pardus,
snow leopard, ounce, Panthera uncia,
jaguar, panther, Panthera onca, Felis onca,
lion, king of beasts, Panthera leo,
tiger, Panthera tigris,
cheetah, chetah, Acinonyx jubatus,
brown bear, bruin, Ursus arctos,
American black bear, black bear, Ursus americanus, Euarctos americanus,
ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus,
sloth bear, Melursus ursinus, Ursus ursinus,
mongoose,
meerkat, mierkat,
tiger beetle,
ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle,
ground beetle, carabid beetle,
long-horned beetle, longicorn, longicorn beetle,
leaf beetle, chrysomelid,
dung beetle,
rhinoceros beetle,
weevil,
fly,
bee,
ant, emmet, pismire,
grasshopper, hopper,
cricket,
walking stick, walkingstick, stick insect,
cockroach, roach,
mantis, mantid,
cicada, cicala,
leafhopper,
lacewing, lacewing fly,
"dragonfly, darning needle, devils darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
damselfly,
admiral,
ringlet, ringlet butterfly,
monarch, monarch butterfly, milkweed butterfly, Danaus plexippus,
cabbage butterfly,
sulphur butterfly, sulfur butterfly,
lycaenid, lycaenid butterfly,
starfish, sea star,
sea urchin,
sea cucumber, holothurian,
wood rabbit, cottontail, cottontail rabbit,
hare,
Angora, Angora rabbit,
hamster,
porcupine, hedgehog,
fox squirrel, eastern fox squirrel, Sciurus niger,
marmot,
beaver,
guinea pig, Cavia cobaya,
sorrel,
zebra,
hog, pig, grunter, squealer, Sus scrofa,
wild boar, boar, Sus scrofa,
warthog,
hippopotamus, hippo, river horse, Hippopotamus amphibius,
ox,
water buffalo, water ox, Asiatic buffalo, Bubalus bubalis,
bison,
ram, tup,
bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis,
ibex, Capra ibex,
hartebeest,
impala, Aepyceros melampus,
gazelle,
Arabian camel, dromedary, Camelus dromedarius,
llama,
weasel,
mink,
polecat, fitch, foulmart, foumart, Mustela putorius,
black-footed ferret, ferret, Mustela nigripes,
otter,
skunk, polecat, wood pussy,
badger,
armadillo,
three-toed sloth, ai, Bradypus tridactylus,
orangutan, orang, orangutang, Pongo pygmaeus,
gorilla, Gorilla gorilla,
chimpanzee, chimp, Pan troglodytes,
gibbon, Hylobates lar,
siamang, Hylobates syndactylus, Symphalangus syndactylus,
guenon, guenon monkey,
patas, hussar monkey, Erythrocebus patas,
baboon,
macaque,
langur,
colobus, colobus monkey,
proboscis monkey, Nasalis larvatus,
marmoset,
capuchin, ringtail, Cebus capucinus,
howler monkey, howler,
titi, titi monkey,
spider monkey, Ateles geoffroyi,
squirrel monkey, Saimiri sciureus,
Madagascar cat, ring-tailed lemur, Lemur catta,
indri, indris, Indri indri, Indri brevicaudatus,
Indian elephant, Elephas maximus,
African elephant, Loxodonta africana,
lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens,
giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca,
barracouta, snoek,
eel,
coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch,
rock beauty, Holocanthus tricolor,
anemone fish,
sturgeon,
gar, garfish, garpike, billfish, Lepisosteus osseus,
lionfish,
puffer, pufferfish, blowfish, globefish,
abacus,
abaya,
"academic gown, academic robe, judges robe",
accordion, piano accordion, squeeze box,
acoustic guitar,
aircraft carrier, carrier, flattop, attack aircraft carrier,
airliner,
airship, dirigible,
altar,
ambulance,
amphibian, amphibious vehicle,
analog clock,
apiary, bee house,
apron,
ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin,
assault rifle, assault gun,
backpack, back pack, knapsack, packsack, rucksack, haversack,
bakery, bakeshop, bakehouse,
balance beam, beam,
balloon,
ballpoint, ballpoint pen, ballpen, Biro,
Band Aid,
banjo,
bannister, banister, balustrade, balusters, handrail,
barbell,
barber chair,
barbershop,
barn,
barometer,
barrel, cask,
barrow, garden cart, lawn cart, wheelbarrow,
baseball,
basketball,
bassinet,
bassoon,
bathing cap, swimming cap,
bath towel,
bathtub, bathing tub, bath, tub,
beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon,
beacon, lighthouse, beacon light, pharos,
beaker,
bearskin, busby, shako,
beer bottle,
beer glass,
bell cote, bell cot,
bib,
bicycle-built-for-two, tandem bicycle, tandem,
bikini, two-piece,
binder, ring-binder,
binoculars, field glasses, opera glasses,
birdhouse,
boathouse,
bobsled, bobsleigh, bob,
bolo tie, bolo, bola tie, bola,
bonnet, poke bonnet,
bookcase,
bookshop, bookstore, bookstall,
bottlecap,
bow,
bow tie, bow-tie, bowtie,
brass, memorial tablet, plaque,
brassiere, bra, bandeau,
breakwater, groin, groyne, mole, bulwark, seawall, jetty,
breastplate, aegis, egis,
broom,
bucket, pail,
buckle,
bulletproof vest,
bullet train, bullet,
butcher shop, meat market,
cab, hack, taxi, taxicab,
caldron, cauldron,
candle, taper, wax light,
cannon,
canoe,
can opener, tin opener,
cardigan,
car mirror,
carousel, carrousel, merry-go-round, roundabout, whirligig,
"carpenters kit, tool kit",
carton,
car wheel,
cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM,
cassette,
cassette player,
castle,
catamaran,
CD player,
cello, violoncello,
cellular telephone, cellular phone, cellphone, cell, mobile phone,
chain,
chainlink fence,
chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour,
chain saw, chainsaw,
chest,
chiffonier, commode,
chime, bell, gong,
china cabinet, china closet,
Christmas stocking,
church, church building,
cinema, movie theater, movie theatre, movie house, picture palace,
cleaver, meat cleaver, chopper,
cliff dwelling,
cloak,
clog, geta, patten, sabot,
cocktail shaker,
coffee mug,
coffeepot,
coil, spiral, volute, whorl, helix,
combination lock,
computer keyboard, keypad,
confectionery, confectionary, candy store,
container ship, containership, container vessel,
convertible,
corkscrew, bottle screw,
cornet, horn, trumpet, trump,
cowboy boot,
cowboy hat, ten-gallon hat,
cradle,
crane,
crash helmet,
crate,
crib, cot,
Crock Pot,
croquet ball,
crutch,
cuirass,
dam, dike, dyke,
desk,
desktop computer,
dial telephone, dial phone,
diaper, nappy, napkin,
digital clock,
digital watch,
dining table, board,
dishrag, dishcloth,
dishwasher, dish washer, dishwashing machine,
disk brake, disc brake,
dock, dockage, docking facility,
dogsled, dog sled, dog sleigh,
dome,
doormat, welcome mat,
drilling platform, offshore rig,
drum, membranophone, tympan,
drumstick,
dumbbell,
Dutch oven,
electric fan, blower,
electric guitar,
electric locomotive,
entertainment center,
envelope,
espresso maker,
face powder,
feather boa, boa,
file, file cabinet, filing cabinet,
fireboat,
fire engine, fire truck,
fire screen, fireguard,
flagpole, flagstaff,
flute, transverse flute,
folding chair,
football helmet,
forklift,
fountain,
fountain pen,
four-poster,
freight car,
French horn, horn,
frying pan, frypan, skillet,
fur coat,
garbage truck, dustcart,
gasmask, respirator, gas helmet,
gas pump, gasoline pump, petrol pump, island dispenser,
goblet,
go-kart,
golf ball,
golfcart, golf cart,
gondola,
gong, tam-tam,
gown,
grand piano, grand,
greenhouse, nursery, glasshouse,
grille, radiator grille,
grocery store, grocery, food market, market,
guillotine,
hair slide,
hair spray,
half track,
hammer,
hamper,
hand blower, blow dryer, blow drier, hair dryer, hair drier,
hand-held computer, hand-held microcomputer,
handkerchief, hankie, hanky, hankey,
hard disc, hard disk, fixed disk,
harmonica, mouth organ, harp, mouth harp,
harp,
harvester, reaper,
hatchet,
holster,
home theater, home theatre,
honeycomb,
hook, claw,
hoopskirt, crinoline,
horizontal bar, high bar,
horse cart, horse-cart,
hourglass,
iPod,
iron, smoothing iron,
"jack-o-lantern",
jean, blue jean, denim,
jeep, landrover,
jersey, T-shirt, tee shirt,
jigsaw puzzle,
jinrikisha, ricksha, rickshaw,
joystick,
kimono,
knee pad,
knot,
lab coat, laboratory coat,
ladle,
lampshade, lamp shade,
laptop, laptop computer,
lawn mower, mower,
lens cap, lens cover,
letter opener, paper knife, paperknife,
library,
lifeboat,
lighter, light, igniter, ignitor,
limousine, limo,
liner, ocean liner,
lipstick, lip rouge,
Loafer,
lotion,
loudspeaker, speaker, speaker unit, loudspeaker system, speaker system,
"loupe, jewelers loupe",
lumbermill, sawmill,
magnetic compass,
mailbag, postbag,
mailbox, letter box,
maillot,
maillot, tank suit,
manhole cover,
maraca,
marimba, xylophone,
mask,
matchstick,
maypole,
maze, labyrinth,
measuring cup,
medicine chest, medicine cabinet,
megalith, megalithic structure,
microphone, mike,
microwave, microwave oven,
military uniform,
milk can,
minibus,
miniskirt, mini,
minivan,
missile,
mitten,
mixing bowl,
mobile home, manufactured home,
Model T,
modem,
monastery,
monitor,
moped,
mortar,
mortarboard,
mosque,
mosquito net,
motor scooter, scooter,
mountain bike, all-terrain bike, off-roader,
mountain tent,
mouse, computer mouse,
mousetrap,
moving van,
muzzle,
nail,
neck brace,
necklace,
nipple,
notebook, notebook computer,
obelisk,
oboe, hautboy, hautbois,
ocarina, sweet potato,
odometer, hodometer, mileometer, milometer,
oil filter,
organ, pipe organ,
oscilloscope, scope, cathode-ray oscilloscope, CRO,
overskirt,
oxcart,
oxygen mask,
packet,
paddle, boat paddle,
paddlewheel, paddle wheel,
padlock,
paintbrush,
"pajama, pyjama, pjs, jammies",
palace,
panpipe, pandean pipe, syrinx,
paper towel,
parachute, chute,
parallel bars, bars,
park bench,
parking meter,
passenger car, coach, carriage,
patio, terrace,
pay-phone, pay-station,
pedestal, plinth, footstall,
pencil box, pencil case,
pencil sharpener,
perfume, essence,
Petri dish,
photocopier,
pick, plectrum, plectron,
pickelhaube,
picket fence, paling,
pickup, pickup truck,
pier,
piggy bank, penny bank,
pill bottle,
pillow,
ping-pong ball,
pinwheel,
pirate, pirate ship,
pitcher, ewer,
"plane, carpenters plane, woodworking plane",
planetarium,
plastic bag,
plate rack,
plow, plough,
"plunger, plumbers helper",
Polaroid camera, Polaroid Land camera,
pole,
police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria,
poncho,
pool table, billiard table, snooker table,
pop bottle, soda bottle,
pot, flowerpot,
"potters wheel",
power drill,
prayer rug, prayer mat,
printer,
prison, prison house,
projectile, missile,
projector,
puck, hockey puck,
punching bag, punch bag, punching ball, punchball,
purse,
quill, quill pen,
quilt, comforter, comfort, puff,
racer, race car, racing car,
racket, racquet,
radiator,
radio, wireless,
radio telescope, radio reflector,
rain barrel,
recreational vehicle, RV, R.V.,
reel,
reflex camera,
refrigerator, icebox,
remote control, remote,
restaurant, eating house, eating place, eatery,
revolver, six-gun, six-shooter,
rifle,
rocking chair, rocker,
rotisserie,
rubber eraser, rubber, pencil eraser,
rugby ball,
rule, ruler,
running shoe,
safe,
safety pin,
saltshaker, salt shaker,
sandal,
sarong,
sax, saxophone,
scabbard,
scale, weighing machine,
school bus,
schooner,
scoreboard,
screen, CRT screen,
screw,
screwdriver,
seat belt, seatbelt,
sewing machine,
shield, buckler,
shoe shop, shoe-shop, shoe store,
shoji,
shopping basket,
shopping cart,
shovel,
shower cap,
shower curtain,
ski,
ski mask,
sleeping bag,
slide rule, slipstick,
sliding door,
slot, one-armed bandit,
snorkel,
snowmobile,
snowplow, snowplough,
soap dispenser,
soccer ball,
sock,
solar dish, solar collector, solar furnace,
sombrero,
soup bowl,
space bar,
space heater,
space shuttle,
spatula,
speedboat,
"spider web, spiders web",
spindle,
sports car, sport car,
spotlight, spot,
stage,
steam locomotive,
steel arch bridge,
steel drum,
stethoscope,
stole,
stone wall,
stopwatch, stop watch,
stove,
strainer,
streetcar, tram, tramcar, trolley, trolley car,
stretcher,
studio couch, day bed,
stupa, tope,
submarine, pigboat, sub, U-boat,
suit, suit of clothes,
sundial,
sunglass,
sunglasses, dark glasses, shades,
sunscreen, sunblock, sun blocker,
suspension bridge,
swab, swob, mop,
sweatshirt,
swimming trunks, bathing trunks,
swing,
switch, electric switch, electrical switch,
syringe,
table lamp,
tank, army tank, armored combat vehicle, armoured combat vehicle,
tape player,
teapot,
teddy, teddy bear,
television, television system,
tennis ball,
thatch, thatched roof,
theater curtain, theatre curtain,
thimble,
thresher, thrasher, threshing machine,
throne,
tile roof,
toaster,
tobacco shop, tobacconist shop, tobacconist,
toilet seat,
torch,
totem pole,
tow truck, tow car, wrecker,
toyshop,
tractor,
trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi,
tray,
trench coat,
tricycle, trike, velocipede,
trimaran,
tripod,
triumphal arch,
trolleybus, trolley coach, trackless trolley,
trombone,
tub, vat,
turnstile,
typewriter keyboard,
umbrella,
unicycle, monocycle,
upright, upright piano,
vacuum, vacuum cleaner,
vase,
vault,
velvet,
vending machine,
vestment,
viaduct,
violin, fiddle,
volleyball,
waffle iron,
wall clock,
wallet, billfold, notecase, pocketbook,
wardrobe, closet, press,
warplane, military plane,
washbasin, handbasin, washbowl, lavabo, wash-hand basin,
washer, automatic washer, washing machine,
water bottle,
water jug,
water tower,
whiskey jug,
whistle,
wig,
window screen,
window shade,
Windsor tie,
wine bottle,
wing,
wok,
wooden spoon,
wool, woolen, woollen,
worm fence, snake fence, snake-rail fence, Virginia fence,
wreck,
yawl,
yurt,
web site, website, internet site, site,
comic book,
crossword puzzle, crossword,
street sign,
traffic light, traffic signal, stoplight,
book jacket, dust cover, dust jacket, dust wrapper,
menu,
plate,
guacamole,
consomme,
hot pot, hotpot,
trifle,
ice cream, icecream,
ice lolly, lolly, lollipop, popsicle,
French loaf,
bagel, beigel,
pretzel,
cheeseburger,
hotdog, hot dog, red hot,
mashed potato,
head cabbage,
broccoli,
cauliflower,
zucchini, courgette,
spaghetti squash,
acorn squash,
butternut squash,
cucumber, cuke,
artichoke, globe artichoke,
bell pepper,
cardoon,
mushroom,
Granny Smith,
strawberry,
orange,
lemon,
fig,
pineapple, ananas,
banana,
jackfruit, jak, jack,
custard apple,
pomegranate,
hay,
carbonara,
chocolate sauce, chocolate syrup,
dough,
meat loaf, meatloaf,
pizza, pizza pie,
potpie,
burrito,
red wine,
espresso,
cup,
eggnog,
alp,
bubble,
cliff, drop, drop-off,
coral reef,
geyser,
lakeside, lakeshore,
promontory, headland, head, foreland,
sandbar, sand bar,
seashore, coast, seacoast, sea-coast,
valley, vale,
volcano,
ballplayer, baseball player,
groom, bridegroom,
scuba diver,
rapeseed,
daisy,
"yellow ladys slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",
corn,
acorn,
hip, rose hip, rosehip,
buckeye, horse chestnut, conker,
coral fungus,
agaric,
gyromitra,
stinkhorn, carrion fungus,
earthstar,
hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa,
bolete,
ear, spike, capitulum,
toilet tissue, toilet paper, bathroom tissue
python/examples/pipeline/PaddleClas/DarkNet53/pipeline_http_client.py 0 → 100644
浏览文件 @ e9ecf97c
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')
if __name__ == "__main__":
url = "http://127.0.0.1:18080/imagenet/prediction"
with open(os.path.join(".", "daisy.jpg"), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
data = {"key": ["image"], "value": [image]}
for i in range(100):
r = requests.post(url=url, data=json.dumps(data))
print(r.json())
python/examples/pipeline/PaddleClas/DarkNet53/pipeline_rpc_client.py 0 → 100644
浏览文件 @ e9ecf97c
# 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.pipeline import PipelineClient
except ImportError:
from paddle_serving_server.pipeline import PipelineClient
import numpy as np
import requests
import json
import cv2
import base64
import os
client = PipelineClient()
client.connect(['127.0.0.1:9993'])
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/PaddleClas/DarkNet53/resnet50_web_service.py 0 → 100644
浏览文件 @ e9ecf97c
# 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
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()
batch_size = len(input_dict.keys())
imgs = []
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)
img = self.seq(im)
imgs.append(img[np.newaxis, :].copy())
input_imgs = np.concatenate(imgs, axis=0)
return {"image": input_imgs}, False, None, ""
def postprocess(self, input_dicts, fetch_dict, log_id):
score_list = fetch_dict["prediction"]
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/pipeline/PaddleClas/HRNet_W18_C/README.md 0 → 100644
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# 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/PaddleClas/HRNet_W18_C/README_CN.md 0 → 100644
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# Imagenet Pipeline WebService
这里以 Imagenet 服务为例来介绍 Pipeline WebService 的使用。
## 获取模型
```
sh get_model.sh
```
## 启动服务
```
python resnet50_web_service.py &>log.txt &
```
## 测试
```
python pipeline_rpc_client.py
```
python/examples/pipeline/PaddleClas/HRNet_W18_C/benchmark.py 0 → 100644
浏览文件 @ e9ecf97c
import sys
import os
import base64
import yaml
import requests
import time
import json
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 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, gpu_id):
fin = open("config.yml", "r")
config = yaml.load(fin)
fin.close()
config["dag"]["tracer"] = {"interval_s": 10}
if device == "gpu":
config["op"]["imagenet"]["local_service_conf"]["device_type"] = 1
config["op"]["imagenet"]["local_service_conf"]["devices"] = gpu_id
else:
config["op"]["imagenet"]["local_service_conf"]["device_type"] = 0
with open("config2.yml", "w") as fout:
yaml.dump(config, fout, default_flow_style=False)
def cv2_to_base64(image):
return base64.b64encode(image).decode('utf8')
def run_http(idx, batch_size):
print("start thread ({})".format(idx))
url = "http://127.0.0.1:18080/imagenet/prediction"
start = time.time()
with open(os.path.join(".", "daisy.jpg"), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
keys, values = [], []
for i in range(batch_size):
keys.append("image_{}".format(i))
values.append(image)
data = {"key": keys, "value": values}
latency_list = []
start_time = time.time()
total_num = 0
while True:
l_start = time.time()
r = requests.post(url=url, data=json.dumps(data))
print(r.json())
l_end = time.time()
latency_list.append(l_end * 1000 - l_start * 1000)
total_num += 1
if time.time() - start_time > 20:
break
end = time.time()
return [[end - start], latency_list, [total_num]]
def multithread_http(thread, batch_size):
multi_thread_runner = MultiThreadRunner()
start = time.time()
result = multi_thread_runner.run(run_http, thread, batch_size)
end = time.time()
total_cost = end - start
avg_cost = 0
total_number = 0
for i in range(thread):
avg_cost += result[0][i]
total_number += result[2][i]
avg_cost = avg_cost / thread
print("Total cost: {}s".format(total_cost))
print("Each thread cost: {}s. ".format(avg_cost))
print("Total count: {}. ".format(total_number))
print("AVG QPS: {} samples/s".format(batch_size * total_number /
total_cost))
show_latency(result[1])
def run_rpc(thread, batch_size):
client = PipelineClient()
client.connect(['127.0.0.1:18080'])
start = time.time()
test_img_dir = "imgs/"
for img_file in os.listdir(test_img_dir):
with open(os.path.join(test_img_dir, img_file), 'rb') as file:
image_data = file.read()
image = cv2_to_base64(image_data)
start_time = time.time()
while True:
ret = client.predict(feed_dict={"image": image}, fetch=["res"])
if time.time() - start_time > 10:
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]
if device == "gpu":
gpu_id = sys.argv[5]
else:
gpu_id = None
gen_yml(device, gpu_id)
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/PaddleClas/HRNet_W18_C/benchmark.sh 0 → 100644
浏览文件 @ e9ecf97c
export FLAGS_profile_pipeline=1
alias python3="python3.6"
modelname="clas-HRNet_W18_C"
# HTTP
#ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3
# Create yaml，If you already have the config.yaml, ignore it.
#python3 benchmark.py yaml local_predictor 1 gpu
rm -rf profile_log_$modelname
echo "Starting HTTP Clients..."
# Start a client in each thread, tesing the case of multiple threads.
for thread_num in 1 2 4 8 12 16
do
for batch_size in 1
do
echo "----${modelname} thread num: ${thread_num} batch size: ${batch_size} mode:http ----" >>profile_log_$modelname
# Start one web service, If you start the service yourself, you can ignore it here.
#python3 web_service.py >web.log 2>&1 &
#sleep 3
# --id is the serial number of the GPU card, Must be the same as the gpu id used by the server.
nvidia-smi --id=3 --query-gpu=memory.used --format=csv -lms 1000 > gpu_use.log 2>&1 &
nvidia-smi --id=3 --query-gpu=utilization.gpu --format=csv -lms 1000 > gpu_utilization.log 2>&1 &
echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
# Start http client
python3 benchmark.py run http $thread_num $batch_size > profile 2>&1
# Collect CPU metrics, Filter data that is zero momentarily, Record the maximum value of GPU memory and the average value of GPU utilization
python3 cpu_utilization.py >> profile_log_$modelname
grep -av '^0 %' gpu_utilization.log > gpu_utilization.log.tmp
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 -F' ' '{sum+=$1} END {print "GPU_UTILIZATION:", sum/NR, sum, NR }' gpu_utilization.log.tmp >> profile_log_$modelname
# Show profiles
python3 ../../../util/show_profile.py profile $thread_num >> profile_log_$modelname
tail -n 8 profile >> profile_log_$modelname
echo '' >> profile_log_$modelname
done
done
# Kill all nvidia-smi background task.
pkill nvidia-smi
python/examples/pipeline/PaddleClas/HRNet_W18_C/benchmark_config.yaml 0 → 100644
浏览文件 @ e9ecf97c
cuda_version: "10.1"
cudnn_version: "7.6"
trt_version: "6.0"
python_version: "3.7"
gcc_version: "8.2"
paddle_version: "2.0.1"
cpu: "Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz X12"
gpu: "T4"
xpu: "None"
api: ""
owner: "cuicheng01"
model_name: "HRNet_W18_C"
model_type: "static"
model_source: "PaddleClas"
model_url: "https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W18_C_pretrained.tar"
batch_size: 1
num_of_samples: 1000
input_shape: "3,224,224"
runtime_device: "gpu"
ir_optim: true
enable_memory_optim: true
enable_tensorrt: false
precision: "fp32"
enable_mkldnn: false
cpu_math_library_num_threads: ""
python/examples/pipeline/PaddleClas/HRNet_W18_C/benchmark_config.yaml.template 0 → 100644
浏览文件 @ e9ecf97c
cuda_version: "10.1"
cudnn_version: "7.6"
trt_version: "6.0"
python_version: "3.7"
gcc_version: "8.2"
paddle_version: "2.0.1"
cpu: "Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz X12"
gpu: "T4"
xpu: "None"
api: ""
owner: "cuicheng01"
model_name: "imagenet"
model_type: "static"
model_source: "PaddleClas"
model_url: "model_url_path"
batch_size: 1
num_of_samples: 1000
input_shape: "3,224,224"
runtime_device: "cpu"
ir_optim: true
enable_memory_optim: true
enable_tensorrt: false
precision: "fp32"
enable_mkldnn: false
cpu_math_library_num_threads: ""
python/examples/pipeline/PaddleClas/HRNet_W18_C/benchmark_gpu.sh 0 → 100644
浏览文件 @ e9ecf97c
export FLAGS_profile_pipeline=1
alias python3="python3.7"
modelname="imagenet"
use_gpu=1
gpu_id="0"
benchmark_config_filename="benchmark_config.yaml"
# HTTP
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3
if [ $use_gpu -eq 1 ]; then
python3 benchmark.py yaml local_predictor 1 gpu $gpu_id
else
python3 benchmark.py yaml local_predictor 1 cpu
fi
rm -rf profile_log_$modelname
for thread_num in 1
do
for batch_size in 1
do
echo "#----imagenet thread num: $thread_num batch size: $batch_size mode:http use_gpu:$use_gpu----" >>profile_log_$modelname
rm -rf PipelineServingLogs
rm -rf cpu_utilization.py
python3 resnet50_web_service.py >web.log 2>&1 &
sleep 3
nvidia-smi --id=${gpu_id} --query-compute-apps=used_memory --format=csv -lms 100 > gpu_use.log 2>&1 &
nvidia-smi --id=${gpu_id} --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
python3 -m paddle_serving_server_gpu.profiler >>profile_log_$modelname
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
ps -ef | grep nvidia-smi | 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=$modelname}} END {print "GPU_MEM:", max}' gpu_use.log >> profile_log_$modelname
awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "GPU_UTIL:", max}' gpu_utilization.log >> profile_log_$modelname
cat benchmark.log >> profile_log_$modelname
python3 -m paddle_serving_server_gpu.parse_profile --benchmark_cfg $benchmark_config_filename --benchmark_log profile_log_$modelname
#rm -rf gpu_use.log gpu_utilization.log
done
done
python/examples/pipeline/PaddleClas/HRNet_W18_C/config.yml 0 → 100644
浏览文件 @ e9ecf97c
#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: 18080
rpc_port: 9993
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: 1
#uci模型路径
model_config: HRNet_W18_C/ppcls_model/
#计算硬件类型: 空缺时由devices决定(CPU/GPU)，0=cpu, 1=gpu, 2=tensorRT, 3=arm cpu, 4=kunlun xpu
device_type: 1
#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: ["prediction"]
python/examples/pipeline/PaddleClas/HRNet_W18_C/cpu_utilization.py 0 → 100644
浏览文件 @ e9ecf97c
import psutil
cpu_utilization=psutil.cpu_percent(1,False)
print('CPU_UTILIZATION:', cpu_utilization)
python/examples/pipeline/PaddleClas/HRNet_W18_C/get_model.sh 0 → 100644
浏览文件 @ e9ecf97c
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/model/HRNet_W18_C.tar
tar -xf HRNet_W18_C.tar
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/PaddleClas/HRNet_W18_C/imagenet.label 0 → 100644
浏览文件 @ e9ecf97c
tench, Tinca tinca,
goldfish, Carassius auratus,
great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias,
tiger shark, Galeocerdo cuvieri,
hammerhead, hammerhead shark,
electric ray, crampfish, numbfish, torpedo,
stingray,
cock,
hen,
ostrich, Struthio camelus,
brambling, Fringilla montifringilla,
goldfinch, Carduelis carduelis,
house finch, linnet, Carpodacus mexicanus,
junco, snowbird,
indigo bunting, indigo finch, indigo bird, Passerina cyanea,
robin, American robin, Turdus migratorius,
bulbul,
jay,
magpie,
chickadee,
water ouzel, dipper,
kite,
bald eagle, American eagle, Haliaeetus leucocephalus,
vulture,
great grey owl, great gray owl, Strix nebulosa,
European fire salamander, Salamandra salamandra,
common newt, Triturus vulgaris,
eft,
spotted salamander, Ambystoma maculatum,
axolotl, mud puppy, Ambystoma mexicanum,
bullfrog, Rana catesbeiana,
tree frog, tree-frog,
tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui,
loggerhead, loggerhead turtle, Caretta caretta,
leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea,
mud turtle,
terrapin,
box turtle, box tortoise,
banded gecko,
common iguana, iguana, Iguana iguana,
American chameleon, anole, Anolis carolinensis,
whiptail, whiptail lizard,
agama,
frilled lizard, Chlamydosaurus kingi,
alligator lizard,
Gila monster, Heloderma suspectum,
green lizard, Lacerta viridis,
African chameleon, Chamaeleo chamaeleon,
Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis,
African crocodile, Nile crocodile, Crocodylus niloticus,
American alligator, Alligator mississipiensis,
triceratops,
thunder snake, worm snake, Carphophis amoenus,
ringneck snake, ring-necked snake, ring snake,
hognose snake, puff adder, sand viper,
green snake, grass snake,
king snake, kingsnake,
garter snake, grass snake,
water snake,
vine snake,
night snake, Hypsiglena torquata,
boa constrictor, Constrictor constrictor,
rock python, rock snake, Python sebae,
Indian cobra, Naja naja,
green mamba,
sea snake,
horned viper, cerastes, sand viper, horned asp, Cerastes cornutus,
diamondback, diamondback rattlesnake, Crotalus adamanteus,
sidewinder, horned rattlesnake, Crotalus cerastes,
trilobite,
harvestman, daddy longlegs, Phalangium opilio,
scorpion,
black and gold garden spider, Argiope aurantia,
barn spider, Araneus cavaticus,
garden spider, Aranea diademata,
black widow, Latrodectus mactans,
tarantula,
wolf spider, hunting spider,
tick,
centipede,
black grouse,
ptarmigan,
ruffed grouse, partridge, Bonasa umbellus,
prairie chicken, prairie grouse, prairie fowl,
peacock,
quail,
partridge,
African grey, African gray, Psittacus erithacus,
macaw,
sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita,
lorikeet,
coucal,
bee eater,
hornbill,
hummingbird,
jacamar,
toucan,
drake,
red-breasted merganser, Mergus serrator,
goose,
black swan, Cygnus atratus,
tusker,
echidna, spiny anteater, anteater,
platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus,
wallaby, brush kangaroo,
koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus,
wombat,
jellyfish,
sea anemone, anemone,
brain coral,
flatworm, platyhelminth,
nematode, nematode worm, roundworm,
conch,
snail,
slug,
sea slug, nudibranch,
chiton, coat-of-mail shell, sea cradle, polyplacophore,
chambered nautilus, pearly nautilus, nautilus,
Dungeness crab, Cancer magister,
rock crab, Cancer irroratus,
fiddler crab,
king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica,
American lobster, Northern lobster, Maine lobster, Homarus americanus,
spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish,
crayfish, crawfish, crawdad, crawdaddy,
hermit crab,
isopod,
white stork, Ciconia ciconia,
black stork, Ciconia nigra,
spoonbill,
flamingo,
little blue heron, Egretta caerulea,
American egret, great white heron, Egretta albus,
bittern,
crane,
limpkin, Aramus pictus,
European gallinule, Porphyrio porphyrio,
American coot, marsh hen, mud hen, water hen, Fulica americana,
bustard,
ruddy turnstone, Arenaria interpres,
red-backed sandpiper, dunlin, Erolia alpina,
redshank, Tringa totanus,
dowitcher,
oystercatcher, oyster catcher,
pelican,
king penguin, Aptenodytes patagonica,
albatross, mollymawk,
grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus,
killer whale, killer, orca, grampus, sea wolf, Orcinus orca,
dugong, Dugong dugon,
sea lion,
Chihuahua,
Japanese spaniel,
Maltese dog, Maltese terrier, Maltese,
Pekinese, Pekingese, Peke,
Shih-Tzu,
Blenheim spaniel,
papillon,
toy terrier,
Rhodesian ridgeback,
Afghan hound, Afghan,
basset, basset hound,
beagle,
bloodhound, sleuthhound,
bluetick,
black-and-tan coonhound,
Walker hound, Walker foxhound,
English foxhound,
redbone,
borzoi, Russian wolfhound,
Irish wolfhound,
Italian greyhound,
whippet,
Ibizan hound, Ibizan Podenco,
Norwegian elkhound, elkhound,
otterhound, otter hound,
Saluki, gazelle hound,
Scottish deerhound, deerhound,
Weimaraner,
Staffordshire bullterrier, Staffordshire bull terrier,
American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier,
Bedlington terrier,
Border terrier,
Kerry blue terrier,
Irish terrier,
Norfolk terrier,
Norwich terrier,
Yorkshire terrier,
wire-haired fox terrier,
Lakeland terrier,
Sealyham terrier, Sealyham,
Airedale, Airedale terrier,
cairn, cairn terrier,
Australian terrier,
Dandie Dinmont, Dandie Dinmont terrier,
Boston bull, Boston terrier,
miniature schnauzer,
giant schnauzer,
standard schnauzer,
Scotch terrier, Scottish terrier, Scottie,
Tibetan terrier, chrysanthemum dog,
silky terrier, Sydney silky,
soft-coated wheaten terrier,
West Highland white terrier,
Lhasa, Lhasa apso,
flat-coated retriever,
curly-coated retriever,
golden retriever,
Labrador retriever,
Chesapeake Bay retriever,
German short-haired pointer,
vizsla, Hungarian pointer,
English setter,
Irish setter, red setter,
Gordon setter,
Brittany spaniel,
clumber, clumber spaniel,
English springer, English springer spaniel,
Welsh springer spaniel,
cocker spaniel, English cocker spaniel, cocker,
Sussex spaniel,
Irish water spaniel,
kuvasz,
schipperke,
groenendael,
malinois,
briard,
kelpie,
komondor,
Old English sheepdog, bobtail,
Shetland sheepdog, Shetland sheep dog, Shetland,
collie,
Border collie,
Bouvier des Flandres, Bouviers des Flandres,
Rottweiler,
German shepherd, German shepherd dog, German police dog, alsatian,
Doberman, Doberman pinscher,
miniature pinscher,
Greater Swiss Mountain dog,
Bernese mountain dog,
Appenzeller,
EntleBucher,
boxer,
bull mastiff,
Tibetan mastiff,
French bulldog,
Great Dane,
Saint Bernard, St Bernard,
Eskimo dog, husky,
malamute, malemute, Alaskan malamute,
Siberian husky,
dalmatian, coach dog, carriage dog,
affenpinscher, monkey pinscher, monkey dog,
basenji,
pug, pug-dog,
Leonberg,
Newfoundland, Newfoundland dog,
Great Pyrenees,
Samoyed, Samoyede,
Pomeranian,
chow, chow chow,
keeshond,
Brabancon griffon,
Pembroke, Pembroke Welsh corgi,
Cardigan, Cardigan Welsh corgi,
toy poodle,
miniature poodle,
standard poodle,
Mexican hairless,
timber wolf, grey wolf, gray wolf, Canis lupus,
white wolf, Arctic wolf, Canis lupus tundrarum,
red wolf, maned wolf, Canis rufus, Canis niger,
coyote, prairie wolf, brush wolf, Canis latrans,
dingo, warrigal, warragal, Canis dingo,
dhole, Cuon alpinus,
African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus,
hyena, hyaena,
red fox, Vulpes vulpes,
kit fox, Vulpes macrotis,
Arctic fox, white fox, Alopex lagopus,
grey fox, gray fox, Urocyon cinereoargenteus,
tabby, tabby cat,
tiger cat,
Persian cat,
Siamese cat, Siamese,
Egyptian cat,
cougar, puma, catamount, mountain lion, painter, panther, Felis concolor,
lynx, catamount,
leopard, Panthera pardus,
snow leopard, ounce, Panthera uncia,
jaguar, panther, Panthera onca, Felis onca,
lion, king of beasts, Panthera leo,
tiger, Panthera tigris,
cheetah, chetah, Acinonyx jubatus,
brown bear, bruin, Ursus arctos,
American black bear, black bear, Ursus americanus, Euarctos americanus,
ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus,
sloth bear, Melursus ursinus, Ursus ursinus,
mongoose,
meerkat, mierkat,
tiger beetle,
ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle,
ground beetle, carabid beetle,
long-horned beetle, longicorn, longicorn beetle,
leaf beetle, chrysomelid,
dung beetle,
rhinoceros beetle,
weevil,
fly,
bee,
ant, emmet, pismire,
grasshopper, hopper,
cricket,
walking stick, walkingstick, stick insect,
cockroach, roach,
mantis, mantid,
cicada, cicala,
leafhopper,
lacewing, lacewing fly,
"dragonfly, darning needle, devils darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
damselfly,
admiral,
ringlet, ringlet butterfly,
monarch, monarch butterfly, milkweed butterfly, Danaus plexippus,
cabbage butterfly,
sulphur butterfly, sulfur butterfly,
lycaenid, lycaenid butterfly,
starfish, sea star,
sea urchin,
sea cucumber, holothurian,
wood rabbit, cottontail, cottontail rabbit,
hare,
Angora, Angora rabbit,
hamster,
porcupine, hedgehog,
fox squirrel, eastern fox squirrel, Sciurus niger,
marmot,
beaver,
guinea pig, Cavia cobaya,
sorrel,
zebra,
hog, pig, grunter, squealer, Sus scrofa,
wild boar, boar, Sus scrofa,
warthog,
hippopotamus, hippo, river horse, Hippopotamus amphibius,
ox,
water buffalo, water ox, Asiatic buffalo, Bubalus bubalis,
bison,
ram, tup,
bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis,
ibex, Capra ibex,
hartebeest,
impala, Aepyceros melampus,
gazelle,
Arabian camel, dromedary, Camelus dromedarius,
llama,
weasel,
mink,
polecat, fitch, foulmart, foumart, Mustela putorius,
black-footed ferret, ferret, Mustela nigripes,
otter,
skunk, polecat, wood pussy,
badger,
armadillo,
three-toed sloth, ai, Bradypus tridactylus,
orangutan, orang, orangutang, Pongo pygmaeus,
gorilla, Gorilla gorilla,
chimpanzee, chimp, Pan troglodytes,
gibbon, Hylobates lar,
siamang, Hylobates syndactylus, Symphalangus syndactylus,
guenon, guenon monkey,
patas, hussar monkey, Erythrocebus patas,
baboon,
macaque,
langur,
colobus, colobus monkey,
proboscis monkey, Nasalis larvatus,
marmoset,
capuchin, ringtail, Cebus capucinus,
howler monkey, howler,
titi, titi monkey,
spider monkey, Ateles geoffroyi,
squirrel monkey, Saimiri sciureus,
Madagascar cat, ring-tailed lemur, Lemur catta,
indri, indris, Indri indri, Indri brevicaudatus,
Indian elephant, Elephas maximus,
African elephant, Loxodonta africana,
lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens,
giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca,
barracouta, snoek,
eel,
coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch,
rock beauty, Holocanthus tricolor,
anemone fish,
sturgeon,
gar, garfish, garpike, billfish, Lepisosteus osseus,
lionfish,
puffer, pufferfish, blowfish, globefish,
abacus,
abaya,
"academic gown, academic robe, judges robe",
accordion, piano accordion, squeeze box,
acoustic guitar,
aircraft carrier, carrier, flattop, attack aircraft carrier,
airliner,
airship, dirigible,
altar,
ambulance,
amphibian, amphibious vehicle,
analog clock,
apiary, bee house,
apron,
ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin,
assault rifle, assault gun,
backpack, back pack, knapsack, packsack, rucksack, haversack,
bakery, bakeshop, bakehouse,
balance beam, beam,
balloon,
ballpoint, ballpoint pen, ballpen, Biro,
Band Aid,
banjo,
bannister, banister, balustrade, balusters, handrail,
barbell,
barber chair,
barbershop,
barn,
barometer,
barrel, cask,
barrow, garden cart, lawn cart, wheelbarrow,
baseball,
basketball,
bassinet,
bassoon,
bathing cap, swimming cap,
bath towel,
bathtub, bathing tub, bath, tub,
beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon,
beacon, lighthouse, beacon light, pharos,
beaker,
bearskin, busby, shako,
beer bottle,
beer glass,
bell cote, bell cot,
bib,
bicycle-built-for-two, tandem bicycle, tandem,
bikini, two-piece,
binder, ring-binder,
binoculars, field glasses, opera glasses,
birdhouse,
boathouse,
bobsled, bobsleigh, bob,
bolo tie, bolo, bola tie, bola,
bonnet, poke bonnet,
bookcase,
bookshop, bookstore, bookstall,
bottlecap,
bow,
bow tie, bow-tie, bowtie,
brass, memorial tablet, plaque,
brassiere, bra, bandeau,
breakwater, groin, groyne, mole, bulwark, seawall, jetty,
breastplate, aegis, egis,
broom,
bucket, pail,
buckle,
bulletproof vest,
bullet train, bullet,
butcher shop, meat market,
cab, hack, taxi, taxicab,
caldron, cauldron,
candle, taper, wax light,
cannon,
canoe,
can opener, tin opener,
cardigan,
car mirror,
carousel, carrousel, merry-go-round, roundabout, whirligig,
"carpenters kit, tool kit",
carton,
car wheel,
cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM,
cassette,
cassette player,
castle,
catamaran,
CD player,
cello, violoncello,
cellular telephone, cellular phone, cellphone, cell, mobile phone,
chain,
chainlink fence,
chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour,
chain saw, chainsaw,
chest,
chiffonier, commode,
chime, bell, gong,
china cabinet, china closet,
Christmas stocking,
church, church building,
cinema, movie theater, movie theatre, movie house, picture palace,
cleaver, meat cleaver, chopper,
cliff dwelling,
cloak,
clog, geta, patten, sabot,
cocktail shaker,
coffee mug,
coffeepot,
coil, spiral, volute, whorl, helix,
combination lock,
computer keyboard, keypad,
confectionery, confectionary, candy store,
container ship, containership, container vessel,
convertible,
corkscrew, bottle screw,
cornet, horn, trumpet, trump,
cowboy boot,
cowboy hat, ten-gallon hat,
cradle,
crane,
crash helmet,
crate,
crib, cot,
Crock Pot,
croquet ball,
crutch,
cuirass,
dam, dike, dyke,
desk,
desktop computer,
dial telephone, dial phone,
diaper, nappy, napkin,
digital clock,
digital watch,
dining table, board,
dishrag, dishcloth,
dishwasher, dish washer, dishwashing machine,
disk brake, disc brake,
dock, dockage, docking facility,
dogsled, dog sled, dog sleigh,
dome,
doormat, welcome mat,
drilling platform, offshore rig,
drum, membranophone, tympan,
drumstick,
dumbbell,
Dutch oven,
electric fan, blower,
electric guitar,
electric locomotive,
entertainment center,
envelope,
espresso maker,
face powder,
feather boa, boa,
file, file cabinet, filing cabinet,
fireboat,
fire engine, fire truck,
fire screen, fireguard,
flagpole, flagstaff,
flute, transverse flute,
folding chair,
football helmet,
forklift,
fountain,
fountain pen,
four-poster,
freight car,
French horn, horn,
frying pan, frypan, skillet,
fur coat,
garbage truck, dustcart,
gasmask, respirator, gas helmet,
gas pump, gasoline pump, petrol pump, island dispenser,
goblet,
go-kart,
golf ball,
golfcart, golf cart,
gondola,
gong, tam-tam,
gown,
grand piano, grand,
greenhouse, nursery, glasshouse,
grille, radiator grille,
grocery store, grocery, food market, market,
guillotine,
hair slide,
hair spray,
half track,
hammer,
hamper,
hand blower, blow dryer, blow drier, hair dryer, hair drier,
hand-held computer, hand-held microcomputer,
handkerchief, hankie, hanky, hankey,
hard disc, hard disk, fixed disk,
harmonica, mouth organ, harp, mouth harp,
harp,
harvester, reaper,
hatchet,
holster,
home theater, home theatre,
honeycomb,
hook, claw,
hoopskirt, crinoline,
horizontal bar, high bar,
horse cart, horse-cart,
hourglass,
iPod,
iron, smoothing iron,
"jack-o-lantern",
jean, blue jean, denim,
jeep, landrover,
jersey, T-shirt, tee shirt,
jigsaw puzzle,
jinrikisha, ricksha, rickshaw,
joystick,
kimono,
knee pad,
knot,
lab coat, laboratory coat,
ladle,
lampshade, lamp shade,
laptop, laptop computer,
lawn mower, mower,
lens cap, lens cover,
letter opener, paper knife, paperknife,
library,
lifeboat,
lighter, light, igniter, ignitor,
limousine, limo,
liner, ocean liner,
lipstick, lip rouge,
Loafer,
lotion,
loudspeaker, speaker, speaker unit, loudspeaker system, speaker system,
"loupe, jewelers loupe",
lumbermill, sawmill,
magnetic compass,
mailbag, postbag,
mailbox, letter box,
maillot,
maillot, tank suit,
manhole cover,
maraca,
marimba, xylophone,
mask,
matchstick,
maypole,
maze, labyrinth,
measuring cup,
medicine chest, medicine cabinet,
megalith, megalithic structure,
microphone, mike,
microwave, microwave oven,
military uniform,
milk can,
minibus,
miniskirt, mini,
minivan,
missile,
mitten,
mixing bowl,
mobile home, manufactured home,
Model T,
modem,
monastery,
monitor,
moped,
mortar,
mortarboard,
mosque,
mosquito net,
motor scooter, scooter,
mountain bike, all-terrain bike, off-roader,
mountain tent,
mouse, computer mouse,
mousetrap,
moving van,
muzzle,
nail,
neck brace,
necklace,
nipple,
notebook, notebook computer,
obelisk,
oboe, hautboy, hautbois,
ocarina, sweet potato,
odometer, hodometer, mileometer, milometer,
oil filter,
organ, pipe organ,
oscilloscope, scope, cathode-ray oscilloscope, CRO,
overskirt,
oxcart,
oxygen mask,
packet,
paddle, boat paddle,
paddlewheel, paddle wheel,
padlock,
paintbrush,
"pajama, pyjama, pjs, jammies",
palace,
panpipe, pandean pipe, syrinx,
paper towel,
parachute, chute,
parallel bars, bars,
park bench,
parking meter,
passenger car, coach, carriage,
patio, terrace,
pay-phone, pay-station,
pedestal, plinth, footstall,
pencil box, pencil case,
pencil sharpener,
perfume, essence,
Petri dish,
photocopier,
pick, plectrum, plectron,
pickelhaube,
picket fence, paling,
pickup, pickup truck,
pier,
piggy bank, penny bank,
pill bottle,
pillow,
ping-pong ball,
pinwheel,
pirate, pirate ship,
pitcher, ewer,
"plane, carpenters plane, woodworking plane",
planetarium,
plastic bag,
plate rack,
plow, plough,
"plunger, plumbers helper",
Polaroid camera, Polaroid Land camera,
pole,
police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria,
poncho,
pool table, billiard table, snooker table,
pop bottle, soda bottle,
pot, flowerpot,
"potters wheel",
power drill,
prayer rug, prayer mat,
printer,
prison, prison house,
projectile, missile,
projector,
puck, hockey puck,
punching bag, punch bag, punching ball, punchball,
purse,
quill, quill pen,
quilt, comforter, comfort, puff,
racer, race car, racing car,
racket, racquet,
radiator,
radio, wireless,
radio telescope, radio reflector,
rain barrel,
recreational vehicle, RV, R.V.,
reel,
reflex camera,
refrigerator, icebox,
remote control, remote,
restaurant, eating house, eating place, eatery,
revolver, six-gun, six-shooter,
rifle,
rocking chair, rocker,
rotisserie,
rubber eraser, rubber, pencil eraser,
rugby ball,
rule, ruler,
running shoe,
safe,
safety pin,
saltshaker, salt shaker,
sandal,
sarong,
sax, saxophone,
scabbard,
scale, weighing machine,
school bus,
schooner,
scoreboard,
screen, CRT screen,
screw,
screwdriver,
seat belt, seatbelt,
sewing machine,
shield, buckler,
shoe shop, shoe-shop, shoe store,
shoji,
shopping basket,
shopping cart,
shovel,
shower cap,
shower curtain,
ski,
ski mask,
sleeping bag,
slide rule, slipstick,
sliding door,
slot, one-armed bandit,
snorkel,
snowmobile,
snowplow, snowplough,
soap dispenser,
soccer ball,
sock,
solar dish, solar collector, solar furnace,
sombrero,
soup bowl,
space bar,
space heater,
space shuttle,
spatula,
speedboat,
"spider web, spiders web",
spindle,
sports car, sport car,
spotlight, spot,
stage,
steam locomotive,
steel arch bridge,
steel drum,
stethoscope,
stole,
stone wall,
stopwatch, stop watch,
stove,
strainer,
streetcar, tram, tramcar, trolley, trolley car,
stretcher,
studio couch, day bed,
stupa, tope,
submarine, pigboat, sub, U-boat,
suit, suit of clothes,
sundial,
sunglass,
sunglasses, dark glasses, shades,
sunscreen, sunblock, sun blocker,
suspension bridge,
swab, swob, mop,
sweatshirt,
swimming trunks, bathing trunks,
swing,
switch, electric switch, electrical switch,
syringe,
table lamp,
tank, army tank, armored combat vehicle, armoured combat vehicle,
tape player,
teapot,
teddy, teddy bear,
television, television system,
tennis ball,
thatch, thatched roof,
theater curtain, theatre curtain,
thimble,
thresher, thrasher, threshing machine,
throne,
tile roof,
toaster,
tobacco shop, tobacconist shop, tobacconist,
toilet seat,
torch,
totem pole,
tow truck, tow car, wrecker,
toyshop,
tractor,
trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi,
tray,
trench coat,
tricycle, trike, velocipede,
trimaran,
tripod,
triumphal arch,
trolleybus, trolley coach, trackless trolley,
trombone,
tub, vat,
turnstile,
typewriter keyboard,
umbrella,
unicycle, monocycle,
upright, upright piano,
vacuum, vacuum cleaner,
vase,
vault,
velvet,
vending machine,
vestment,
viaduct,
violin, fiddle,
volleyball,
waffle iron,
wall clock,
wallet, billfold, notecase, pocketbook,
wardrobe, closet, press,
warplane, military plane,
washbasin, handbasin, washbowl, lavabo, wash-hand basin,
washer, automatic washer, washing machine,
water bottle,
water jug,
water tower,
whiskey jug,
whistle,
wig,
window screen,
window shade,
Windsor tie,
wine bottle,
wing,
wok,
wooden spoon,
wool, woolen, woollen,
worm fence, snake fence, snake-rail fence, Virginia fence,
wreck,
yawl,
yurt,
web site, website, internet site, site,
comic book,
crossword puzzle, crossword,
street sign,
traffic light, traffic signal, stoplight,
book jacket, dust cover, dust jacket, dust wrapper,
menu,
plate,
guacamole,
consomme,
hot pot, hotpot,
trifle,
ice cream, icecream,
ice lolly, lolly, lollipop, popsicle,
French loaf,
bagel, beigel,
pretzel,
cheeseburger,
hotdog, hot dog, red hot,
mashed potato,
head cabbage,
broccoli,
cauliflower,
zucchini, courgette,
spaghetti squash,
acorn squash,
butternut squash,
cucumber, cuke,
artichoke, globe artichoke,
bell pepper,
cardoon,
mushroom,
Granny Smith,
strawberry,
orange,
lemon,
fig,
pineapple, ananas,
banana,
jackfruit, jak, jack,
custard apple,
pomegranate,
hay,
carbonara,
chocolate sauce, chocolate syrup,
dough,
meat loaf, meatloaf,
pizza, pizza pie,
potpie,
burrito,
red wine,
espresso,
cup,
eggnog,
alp,
bubble,
cliff, drop, drop-off,
coral reef,
geyser,
lakeside, lakeshore,
promontory, headland, head, foreland,
sandbar, sand bar,
seashore, coast, seacoast, sea-coast,
valley, vale,
volcano,
ballplayer, baseball player,
groom, bridegroom,
scuba diver,
rapeseed,
daisy,
"yellow ladys slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",
corn,
acorn,
hip, rose hip, rosehip,
buckeye, horse chestnut, conker,
coral fungus,
agaric,
gyromitra,
stinkhorn, carrion fungus,
earthstar,
hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa,
bolete,
ear, spike, capitulum,
toilet tissue, toilet paper, bathroom tissue
python/examples/pipeline/PaddleClas/HRNet_W18_C/pipeline_http_client.py 0 → 100644
浏览文件 @ e9ecf97c
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')
if __name__ == "__main__":
url = "http://127.0.0.1:18080/imagenet/prediction"
with open(os.path.join(".", "daisy.jpg"), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
data = {"key": ["image"], "value": [image]}
for i in range(100):
r = requests.post(url=url, data=json.dumps(data))
print(r.json())
python/examples/pipeline/PaddleClas/HRNet_W18_C/pipeline_rpc_client.py 0 → 100644
浏览文件 @ e9ecf97c
# 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.pipeline import PipelineClient
except ImportError:
from paddle_serving_server.pipeline import PipelineClient
import numpy as np
import requests
import json
import cv2
import base64
import os
client = PipelineClient()
client.connect(['127.0.0.1:9993'])
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/PaddleClas/HRNet_W18_C/resnet50_web_service.py 0 → 100644
浏览文件 @ e9ecf97c
# 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()
batch_size = len(input_dict.keys())
imgs = []
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)
img = self.seq(im)
imgs.append(img[np.newaxis, :].copy())
input_imgs = np.concatenate(imgs, axis=0)
return {"image": input_imgs}, False, None, ""
def postprocess(self, input_dicts, fetch_dict, log_id):
score_list = fetch_dict["prediction"]
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/pipeline/PaddleClas/MobileNetV1/README.md 0 → 100644
浏览文件 @ e9ecf97c
# 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/PaddleClas/MobileNetV1/README_CN.md 0 → 100644
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# Imagenet Pipeline WebService
这里以 Imagenet 服务为例来介绍 Pipeline WebService 的使用。
## 获取模型
```
sh get_model.sh
```
## 启动服务
```
python resnet50_web_service.py &>log.txt &
```
## 测试
```
python pipeline_rpc_client.py
```
python/examples/pipeline/PaddleClas/MobileNetV1/benchmark.py 0 → 100644
浏览文件 @ e9ecf97c
import sys
import os
import base64
import yaml
import requests
import time
import json
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 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, gpu_id):
fin = open("config.yml", "r")
config = yaml.load(fin)
fin.close()
config["dag"]["tracer"] = {"interval_s": 10}
if device == "gpu":
config["op"]["imagenet"]["local_service_conf"]["device_type"] = 1
config["op"]["imagenet"]["local_service_conf"]["devices"] = gpu_id
else:
config["op"]["imagenet"]["local_service_conf"]["device_type"] = 0
with open("config2.yml", "w") as fout:
yaml.dump(config, fout, default_flow_style=False)
def cv2_to_base64(image):
return base64.b64encode(image).decode('utf8')
def run_http(idx, batch_size):
print("start thread ({})".format(idx))
url = "http://127.0.0.1:18080/imagenet/prediction"
start = time.time()
with open(os.path.join(".", "daisy.jpg"), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
keys, values = [], []
for i in range(batch_size):
keys.append("image_{}".format(i))
values.append(image)
data = {"key": keys, "value": values}
latency_list = []
start_time = time.time()
total_num = 0
while True:
l_start = time.time()
r = requests.post(url=url, data=json.dumps(data))
print(r.json())
l_end = time.time()
latency_list.append(l_end * 1000 - l_start * 1000)
total_num += 1
if time.time() - start_time > 20:
break
end = time.time()
return [[end - start], latency_list, [total_num]]
def multithread_http(thread, batch_size):
multi_thread_runner = MultiThreadRunner()
start = time.time()
result = multi_thread_runner.run(run_http, thread, batch_size)
end = time.time()
total_cost = end - start
avg_cost = 0
total_number = 0
for i in range(thread):
avg_cost += result[0][i]
total_number += result[2][i]
avg_cost = avg_cost / thread
print("Total cost: {}s".format(total_cost))
print("Each thread cost: {}s. ".format(avg_cost))
print("Total count: {}. ".format(total_number))
print("AVG QPS: {} samples/s".format(batch_size * total_number /
total_cost))
show_latency(result[1])
def run_rpc(thread, batch_size):
client = PipelineClient()
client.connect(['127.0.0.1:18080'])
start = time.time()
test_img_dir = "imgs/"
for img_file in os.listdir(test_img_dir):
with open(os.path.join(test_img_dir, img_file), 'rb') as file:
image_data = file.read()
image = cv2_to_base64(image_data)
start_time = time.time()
while True:
ret = client.predict(feed_dict={"image": image}, fetch=["res"])
if time.time() - start_time > 10:
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]
if device == "gpu":
gpu_id = sys.argv[5]
else:
gpu_id = None
gen_yml(device, gpu_id)
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/PaddleClas/MobileNetV1/benchmark.sh 0 → 100644
浏览文件 @ e9ecf97c
export FLAGS_profile_pipeline=1
alias python3="python3.6"
modelname="clas-MobileNetV1"
# HTTP
#ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3
# Create yaml，If you already have the config.yaml, ignore it.
#python3 benchmark.py yaml local_predictor 1 gpu
rm -rf profile_log_$modelname
echo "Starting HTTP Clients..."
# Start a client in each thread, tesing the case of multiple threads.
for thread_num in 1 2 4 8 12 16
do
for batch_size in 1
do
echo "----${modelname} thread num: ${thread_num} batch size: ${batch_size} mode:http ----" >>profile_log_$modelname
# Start one web service, If you start the service yourself, you can ignore it here.
#python3 web_service.py >web.log 2>&1 &
#sleep 3
# --id is the serial number of the GPU card, Must be the same as the gpu id used by the server.
nvidia-smi --id=3 --query-gpu=memory.used --format=csv -lms 1000 > gpu_use.log 2>&1 &
nvidia-smi --id=3 --query-gpu=utilization.gpu --format=csv -lms 1000 > gpu_utilization.log 2>&1 &
echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
# Start http client
python3 benchmark.py run http $thread_num $batch_size > profile 2>&1
# Collect CPU metrics, Filter data that is zero momentarily, Record the maximum value of GPU memory and the average value of GPU utilization
python3 cpu_utilization.py >> profile_log_$modelname
grep -av '^0 %' gpu_utilization.log > gpu_utilization.log.tmp
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 -F' ' '{sum+=$1} END {print "GPU_UTILIZATION:", sum/NR, sum, NR }' gpu_utilization.log.tmp >> profile_log_$modelname
# Show profiles
python3 ../../../util/show_profile.py profile $thread_num >> profile_log_$modelname
tail -n 8 profile >> profile_log_$modelname
echo '' >> profile_log_$modelname
done
done
# Kill all nvidia-smi background task.
pkill nvidia-smi
python/examples/pipeline/PaddleClas/MobileNetV1/benchmark_config.yaml 0 → 100644
浏览文件 @ e9ecf97c
cuda_version: "10.1"
cudnn_version: "7.6"
trt_version: "6.0"
python_version: "3.7"
gcc_version: "8.2"
paddle_version: "2.0.1"
cpu: "Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz X12"
gpu: "T4"
xpu: "None"
api: ""
owner: "cuicheng01"
model_name: "MobileNetV1"
model_type: "static"
model_source: "PaddleClas"
model_url: "https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar"
batch_size: 1
num_of_samples: 1000
input_shape: "3,224,224"
runtime_device: "gpu"
ir_optim: true
enable_memory_optim: true
enable_tensorrt: false
precision: "fp32"
enable_mkldnn: false
cpu_math_library_num_threads: ""
python/examples/pipeline/PaddleClas/MobileNetV1/benchmark_config.yaml.template 0 → 100644
浏览文件 @ e9ecf97c
cuda_version: "10.1"
cudnn_version: "7.6"
trt_version: "6.0"
python_version: "3.7"
gcc_version: "8.2"
paddle_version: "2.0.1"
cpu: "Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz X12"
gpu: "T4"
xpu: "None"
api: ""
owner: "cuicheng01"
model_name: "imagenet"
model_type: "static"
model_source: "PaddleClas"
model_url: "model_url_path"
batch_size: 1
num_of_samples: 1000
input_shape: "3,224,224"
runtime_device: "cpu"
ir_optim: true
enable_memory_optim: true
enable_tensorrt: false
precision: "fp32"
enable_mkldnn: false
cpu_math_library_num_threads: ""
python/examples/pipeline/PaddleClas/MobileNetV1/benchmark_gpu.sh 0 → 100644
浏览文件 @ e9ecf97c
export FLAGS_profile_pipeline=1
alias python3="python3.7"
modelname="imagenet"
use_gpu=1
gpu_id="0"
benchmark_config_filename="benchmark_config.yaml"
# HTTP
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3
if [ $use_gpu -eq 1 ]; then
python3 benchmark.py yaml local_predictor 1 gpu $gpu_id
else
python3 benchmark.py yaml local_predictor 1 cpu
fi
rm -rf profile_log_$modelname
for thread_num in 1
do
for batch_size in 1
do
echo "#----imagenet thread num: $thread_num batch size: $batch_size mode:http use_gpu:$use_gpu----" >>profile_log_$modelname
rm -rf PipelineServingLogs
rm -rf cpu_utilization.py
python3 resnet50_web_service.py >web.log 2>&1 &
sleep 3
nvidia-smi --id=${gpu_id} --query-compute-apps=used_memory --format=csv -lms 100 > gpu_use.log 2>&1 &
nvidia-smi --id=${gpu_id} --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
python3 -m paddle_serving_server_gpu.profiler >>profile_log_$modelname
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
ps -ef | grep nvidia-smi | 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=$modelname}} END {print "GPU_MEM:", max}' gpu_use.log >> profile_log_$modelname
awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "GPU_UTIL:", max}' gpu_utilization.log >> profile_log_$modelname
cat benchmark.log >> profile_log_$modelname
python3 -m paddle_serving_server_gpu.parse_profile --benchmark_cfg $benchmark_config_filename --benchmark_log profile_log_$modelname
#rm -rf gpu_use.log gpu_utilization.log
done
done
python/examples/pipeline/PaddleClas/MobileNetV1/config.yml 0 → 100644
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#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: 18080
rpc_port: 9993
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: 1
#uci模型路径
model_config: MobileNetV1/ppcls_model/
#计算硬件类型: 空缺时由devices决定(CPU/GPU)，0=cpu, 1=gpu, 2=tensorRT, 3=arm cpu, 4=kunlun xpu
device_type: 1
#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: ["prediction"]
python/examples/pipeline/PaddleClas/MobileNetV1/cpu_utilization.py 0 → 100644
浏览文件 @ e9ecf97c
import psutil
cpu_utilization=psutil.cpu_percent(1,False)
print('CPU_UTILIZATION:', cpu_utilization)
python/examples/pipeline/PaddleClas/MobileNetV1/get_model.sh 0 → 100644
浏览文件 @ e9ecf97c
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/model/MobileNetV1.tar
tar -xf MobileNetV1.tar
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/PaddleClas/MobileNetV1/imagenet.label 0 → 100644
浏览文件 @ e9ecf97c
tench, Tinca tinca,
goldfish, Carassius auratus,
great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias,
tiger shark, Galeocerdo cuvieri,
hammerhead, hammerhead shark,
electric ray, crampfish, numbfish, torpedo,
stingray,
cock,
hen,
ostrich, Struthio camelus,
brambling, Fringilla montifringilla,
goldfinch, Carduelis carduelis,
house finch, linnet, Carpodacus mexicanus,
junco, snowbird,
indigo bunting, indigo finch, indigo bird, Passerina cyanea,
robin, American robin, Turdus migratorius,
bulbul,
jay,
magpie,
chickadee,
water ouzel, dipper,
kite,
bald eagle, American eagle, Haliaeetus leucocephalus,
vulture,
great grey owl, great gray owl, Strix nebulosa,
European fire salamander, Salamandra salamandra,
common newt, Triturus vulgaris,
eft,
spotted salamander, Ambystoma maculatum,
axolotl, mud puppy, Ambystoma mexicanum,
bullfrog, Rana catesbeiana,
tree frog, tree-frog,
tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui,
loggerhead, loggerhead turtle, Caretta caretta,
leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea,
mud turtle,
terrapin,
box turtle, box tortoise,
banded gecko,
common iguana, iguana, Iguana iguana,
American chameleon, anole, Anolis carolinensis,
whiptail, whiptail lizard,
agama,
frilled lizard, Chlamydosaurus kingi,
alligator lizard,
Gila monster, Heloderma suspectum,
green lizard, Lacerta viridis,
African chameleon, Chamaeleo chamaeleon,
Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis,
African crocodile, Nile crocodile, Crocodylus niloticus,
American alligator, Alligator mississipiensis,
triceratops,
thunder snake, worm snake, Carphophis amoenus,
ringneck snake, ring-necked snake, ring snake,
hognose snake, puff adder, sand viper,
green snake, grass snake,
king snake, kingsnake,
garter snake, grass snake,
water snake,
vine snake,
night snake, Hypsiglena torquata,
boa constrictor, Constrictor constrictor,
rock python, rock snake, Python sebae,
Indian cobra, Naja naja,
green mamba,
sea snake,
horned viper, cerastes, sand viper, horned asp, Cerastes cornutus,
diamondback, diamondback rattlesnake, Crotalus adamanteus,
sidewinder, horned rattlesnake, Crotalus cerastes,
trilobite,
harvestman, daddy longlegs, Phalangium opilio,
scorpion,
black and gold garden spider, Argiope aurantia,
barn spider, Araneus cavaticus,
garden spider, Aranea diademata,
black widow, Latrodectus mactans,
tarantula,
wolf spider, hunting spider,
tick,
centipede,
black grouse,
ptarmigan,
ruffed grouse, partridge, Bonasa umbellus,
prairie chicken, prairie grouse, prairie fowl,
peacock,
quail,
partridge,
African grey, African gray, Psittacus erithacus,
macaw,
sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita,
lorikeet,
coucal,
bee eater,
hornbill,
hummingbird,
jacamar,
toucan,
drake,
red-breasted merganser, Mergus serrator,
goose,
black swan, Cygnus atratus,
tusker,
echidna, spiny anteater, anteater,
platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus,
wallaby, brush kangaroo,
koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus,
wombat,
jellyfish,
sea anemone, anemone,
brain coral,
flatworm, platyhelminth,
nematode, nematode worm, roundworm,
conch,
snail,
slug,
sea slug, nudibranch,
chiton, coat-of-mail shell, sea cradle, polyplacophore,
chambered nautilus, pearly nautilus, nautilus,
Dungeness crab, Cancer magister,
rock crab, Cancer irroratus,
fiddler crab,
king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica,
American lobster, Northern lobster, Maine lobster, Homarus americanus,
spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish,
crayfish, crawfish, crawdad, crawdaddy,
hermit crab,
isopod,
white stork, Ciconia ciconia,
black stork, Ciconia nigra,
spoonbill,
flamingo,
little blue heron, Egretta caerulea,
American egret, great white heron, Egretta albus,
bittern,
crane,
limpkin, Aramus pictus,
European gallinule, Porphyrio porphyrio,
American coot, marsh hen, mud hen, water hen, Fulica americana,
bustard,
ruddy turnstone, Arenaria interpres,
red-backed sandpiper, dunlin, Erolia alpina,
redshank, Tringa totanus,
dowitcher,
oystercatcher, oyster catcher,
pelican,
king penguin, Aptenodytes patagonica,
albatross, mollymawk,
grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus,
killer whale, killer, orca, grampus, sea wolf, Orcinus orca,
dugong, Dugong dugon,
sea lion,
Chihuahua,
Japanese spaniel,
Maltese dog, Maltese terrier, Maltese,
Pekinese, Pekingese, Peke,
Shih-Tzu,
Blenheim spaniel,
papillon,
toy terrier,
Rhodesian ridgeback,
Afghan hound, Afghan,
basset, basset hound,
beagle,
bloodhound, sleuthhound,
bluetick,
black-and-tan coonhound,
Walker hound, Walker foxhound,
English foxhound,
redbone,
borzoi, Russian wolfhound,
Irish wolfhound,
Italian greyhound,
whippet,
Ibizan hound, Ibizan Podenco,
Norwegian elkhound, elkhound,
otterhound, otter hound,
Saluki, gazelle hound,
Scottish deerhound, deerhound,
Weimaraner,
Staffordshire bullterrier, Staffordshire bull terrier,
American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier,
Bedlington terrier,
Border terrier,
Kerry blue terrier,
Irish terrier,
Norfolk terrier,
Norwich terrier,
Yorkshire terrier,
wire-haired fox terrier,
Lakeland terrier,
Sealyham terrier, Sealyham,
Airedale, Airedale terrier,
cairn, cairn terrier,
Australian terrier,
Dandie Dinmont, Dandie Dinmont terrier,
Boston bull, Boston terrier,
miniature schnauzer,
giant schnauzer,
standard schnauzer,
Scotch terrier, Scottish terrier, Scottie,
Tibetan terrier, chrysanthemum dog,
silky terrier, Sydney silky,
soft-coated wheaten terrier,
West Highland white terrier,
Lhasa, Lhasa apso,
flat-coated retriever,
curly-coated retriever,
golden retriever,
Labrador retriever,
Chesapeake Bay retriever,
German short-haired pointer,
vizsla, Hungarian pointer,
English setter,
Irish setter, red setter,
Gordon setter,
Brittany spaniel,
clumber, clumber spaniel,
English springer, English springer spaniel,
Welsh springer spaniel,
cocker spaniel, English cocker spaniel, cocker,
Sussex spaniel,
Irish water spaniel,
kuvasz,
schipperke,
groenendael,
malinois,
briard,
kelpie,
komondor,
Old English sheepdog, bobtail,
Shetland sheepdog, Shetland sheep dog, Shetland,
collie,
Border collie,
Bouvier des Flandres, Bouviers des Flandres,
Rottweiler,
German shepherd, German shepherd dog, German police dog, alsatian,
Doberman, Doberman pinscher,
miniature pinscher,
Greater Swiss Mountain dog,
Bernese mountain dog,
Appenzeller,
EntleBucher,
boxer,
bull mastiff,
Tibetan mastiff,
French bulldog,
Great Dane,
Saint Bernard, St Bernard,
Eskimo dog, husky,
malamute, malemute, Alaskan malamute,
Siberian husky,
dalmatian, coach dog, carriage dog,
affenpinscher, monkey pinscher, monkey dog,
basenji,
pug, pug-dog,
Leonberg,
Newfoundland, Newfoundland dog,
Great Pyrenees,
Samoyed, Samoyede,
Pomeranian,
chow, chow chow,
keeshond,
Brabancon griffon,
Pembroke, Pembroke Welsh corgi,
Cardigan, Cardigan Welsh corgi,
toy poodle,
miniature poodle,
standard poodle,
Mexican hairless,
timber wolf, grey wolf, gray wolf, Canis lupus,
white wolf, Arctic wolf, Canis lupus tundrarum,
red wolf, maned wolf, Canis rufus, Canis niger,
coyote, prairie wolf, brush wolf, Canis latrans,
dingo, warrigal, warragal, Canis dingo,
dhole, Cuon alpinus,
African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus,
hyena, hyaena,
red fox, Vulpes vulpes,
kit fox, Vulpes macrotis,
Arctic fox, white fox, Alopex lagopus,
grey fox, gray fox, Urocyon cinereoargenteus,
tabby, tabby cat,
tiger cat,
Persian cat,
Siamese cat, Siamese,
Egyptian cat,
cougar, puma, catamount, mountain lion, painter, panther, Felis concolor,
lynx, catamount,
leopard, Panthera pardus,
snow leopard, ounce, Panthera uncia,
jaguar, panther, Panthera onca, Felis onca,
lion, king of beasts, Panthera leo,
tiger, Panthera tigris,
cheetah, chetah, Acinonyx jubatus,
brown bear, bruin, Ursus arctos,
American black bear, black bear, Ursus americanus, Euarctos americanus,
ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus,
sloth bear, Melursus ursinus, Ursus ursinus,
mongoose,
meerkat, mierkat,
tiger beetle,
ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle,
ground beetle, carabid beetle,
long-horned beetle, longicorn, longicorn beetle,
leaf beetle, chrysomelid,
dung beetle,
rhinoceros beetle,
weevil,
fly,
bee,
ant, emmet, pismire,
grasshopper, hopper,
cricket,
walking stick, walkingstick, stick insect,
cockroach, roach,
mantis, mantid,
cicada, cicala,
leafhopper,
lacewing, lacewing fly,
"dragonfly, darning needle, devils darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
damselfly,
admiral,
ringlet, ringlet butterfly,
monarch, monarch butterfly, milkweed butterfly, Danaus plexippus,
cabbage butterfly,
sulphur butterfly, sulfur butterfly,
lycaenid, lycaenid butterfly,
starfish, sea star,
sea urchin,
sea cucumber, holothurian,
wood rabbit, cottontail, cottontail rabbit,
hare,
Angora, Angora rabbit,
hamster,
porcupine, hedgehog,
fox squirrel, eastern fox squirrel, Sciurus niger,
marmot,
beaver,
guinea pig, Cavia cobaya,
sorrel,
zebra,
hog, pig, grunter, squealer, Sus scrofa,
wild boar, boar, Sus scrofa,
warthog,
hippopotamus, hippo, river horse, Hippopotamus amphibius,
ox,
water buffalo, water ox, Asiatic buffalo, Bubalus bubalis,
bison,
ram, tup,
bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis,
ibex, Capra ibex,
hartebeest,
impala, Aepyceros melampus,
gazelle,
Arabian camel, dromedary, Camelus dromedarius,
llama,
weasel,
mink,
polecat, fitch, foulmart, foumart, Mustela putorius,
black-footed ferret, ferret, Mustela nigripes,
otter,
skunk, polecat, wood pussy,
badger,
armadillo,
three-toed sloth, ai, Bradypus tridactylus,
orangutan, orang, orangutang, Pongo pygmaeus,
gorilla, Gorilla gorilla,
chimpanzee, chimp, Pan troglodytes,
gibbon, Hylobates lar,
siamang, Hylobates syndactylus, Symphalangus syndactylus,
guenon, guenon monkey,
patas, hussar monkey, Erythrocebus patas,
baboon,
macaque,
langur,
colobus, colobus monkey,
proboscis monkey, Nasalis larvatus,
marmoset,
capuchin, ringtail, Cebus capucinus,
howler monkey, howler,
titi, titi monkey,
spider monkey, Ateles geoffroyi,
squirrel monkey, Saimiri sciureus,
Madagascar cat, ring-tailed lemur, Lemur catta,
indri, indris, Indri indri, Indri brevicaudatus,
Indian elephant, Elephas maximus,
African elephant, Loxodonta africana,
lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens,
giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca,
barracouta, snoek,
eel,
coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch,
rock beauty, Holocanthus tricolor,
anemone fish,
sturgeon,
gar, garfish, garpike, billfish, Lepisosteus osseus,
lionfish,
puffer, pufferfish, blowfish, globefish,
abacus,
abaya,
"academic gown, academic robe, judges robe",
accordion, piano accordion, squeeze box,
acoustic guitar,
aircraft carrier, carrier, flattop, attack aircraft carrier,
airliner,
airship, dirigible,
altar,
ambulance,
amphibian, amphibious vehicle,
analog clock,
apiary, bee house,
apron,
ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin,
assault rifle, assault gun,
backpack, back pack, knapsack, packsack, rucksack, haversack,
bakery, bakeshop, bakehouse,
balance beam, beam,
balloon,
ballpoint, ballpoint pen, ballpen, Biro,
Band Aid,
banjo,
bannister, banister, balustrade, balusters, handrail,
barbell,
barber chair,
barbershop,
barn,
barometer,
barrel, cask,
barrow, garden cart, lawn cart, wheelbarrow,
baseball,
basketball,
bassinet,
bassoon,
bathing cap, swimming cap,
bath towel,
bathtub, bathing tub, bath, tub,
beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon,
beacon, lighthouse, beacon light, pharos,
beaker,
bearskin, busby, shako,
beer bottle,
beer glass,
bell cote, bell cot,
bib,
bicycle-built-for-two, tandem bicycle, tandem,
bikini, two-piece,
binder, ring-binder,
binoculars, field glasses, opera glasses,
birdhouse,
boathouse,
bobsled, bobsleigh, bob,
bolo tie, bolo, bola tie, bola,
bonnet, poke bonnet,
bookcase,
bookshop, bookstore, bookstall,
bottlecap,
bow,
bow tie, bow-tie, bowtie,
brass, memorial tablet, plaque,
brassiere, bra, bandeau,
breakwater, groin, groyne, mole, bulwark, seawall, jetty,
breastplate, aegis, egis,
broom,
bucket, pail,
buckle,
bulletproof vest,
bullet train, bullet,
butcher shop, meat market,
cab, hack, taxi, taxicab,
caldron, cauldron,
candle, taper, wax light,
cannon,
canoe,
can opener, tin opener,
cardigan,
car mirror,
carousel, carrousel, merry-go-round, roundabout, whirligig,
"carpenters kit, tool kit",
carton,
car wheel,
cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM,
cassette,
cassette player,
castle,
catamaran,
CD player,
cello, violoncello,
cellular telephone, cellular phone, cellphone, cell, mobile phone,
chain,
chainlink fence,
chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour,
chain saw, chainsaw,
chest,
chiffonier, commode,
chime, bell, gong,
china cabinet, china closet,
Christmas stocking,
church, church building,
cinema, movie theater, movie theatre, movie house, picture palace,
cleaver, meat cleaver, chopper,
cliff dwelling,
cloak,
clog, geta, patten, sabot,
cocktail shaker,
coffee mug,
coffeepot,
coil, spiral, volute, whorl, helix,
combination lock,
computer keyboard, keypad,
confectionery, confectionary, candy store,
container ship, containership, container vessel,
convertible,
corkscrew, bottle screw,
cornet, horn, trumpet, trump,
cowboy boot,
cowboy hat, ten-gallon hat,
cradle,
crane,
crash helmet,
crate,
crib, cot,
Crock Pot,
croquet ball,
crutch,
cuirass,
dam, dike, dyke,
desk,
desktop computer,
dial telephone, dial phone,
diaper, nappy, napkin,
digital clock,
digital watch,
dining table, board,
dishrag, dishcloth,
dishwasher, dish washer, dishwashing machine,
disk brake, disc brake,
dock, dockage, docking facility,
dogsled, dog sled, dog sleigh,
dome,
doormat, welcome mat,
drilling platform, offshore rig,
drum, membranophone, tympan,
drumstick,
dumbbell,
Dutch oven,
electric fan, blower,
electric guitar,
electric locomotive,
entertainment center,
envelope,
espresso maker,
face powder,
feather boa, boa,
file, file cabinet, filing cabinet,
fireboat,
fire engine, fire truck,
fire screen, fireguard,
flagpole, flagstaff,
flute, transverse flute,
folding chair,
football helmet,
forklift,
fountain,
fountain pen,
four-poster,
freight car,
French horn, horn,
frying pan, frypan, skillet,
fur coat,
garbage truck, dustcart,
gasmask, respirator, gas helmet,
gas pump, gasoline pump, petrol pump, island dispenser,
goblet,
go-kart,
golf ball,
golfcart, golf cart,
gondola,
gong, tam-tam,
gown,
grand piano, grand,
greenhouse, nursery, glasshouse,
grille, radiator grille,
grocery store, grocery, food market, market,
guillotine,
hair slide,
hair spray,
half track,
hammer,
hamper,
hand blower, blow dryer, blow drier, hair dryer, hair drier,
hand-held computer, hand-held microcomputer,
handkerchief, hankie, hanky, hankey,
hard disc, hard disk, fixed disk,
harmonica, mouth organ, harp, mouth harp,
harp,
harvester, reaper,
hatchet,
holster,
home theater, home theatre,
honeycomb,
hook, claw,
hoopskirt, crinoline,
horizontal bar, high bar,
horse cart, horse-cart,
hourglass,
iPod,
iron, smoothing iron,
"jack-o-lantern",
jean, blue jean, denim,
jeep, landrover,
jersey, T-shirt, tee shirt,
jigsaw puzzle,
jinrikisha, ricksha, rickshaw,
joystick,
kimono,
knee pad,
knot,
lab coat, laboratory coat,
ladle,
lampshade, lamp shade,
laptop, laptop computer,
lawn mower, mower,
lens cap, lens cover,
letter opener, paper knife, paperknife,
library,
lifeboat,
lighter, light, igniter, ignitor,
limousine, limo,
liner, ocean liner,
lipstick, lip rouge,
Loafer,
lotion,
loudspeaker, speaker, speaker unit, loudspeaker system, speaker system,
"loupe, jewelers loupe",
lumbermill, sawmill,
magnetic compass,
mailbag, postbag,
mailbox, letter box,
maillot,
maillot, tank suit,
manhole cover,
maraca,
marimba, xylophone,
mask,
matchstick,
maypole,
maze, labyrinth,
measuring cup,
medicine chest, medicine cabinet,
megalith, megalithic structure,
microphone, mike,
microwave, microwave oven,
military uniform,
milk can,
minibus,
miniskirt, mini,
minivan,
missile,
mitten,
mixing bowl,
mobile home, manufactured home,
Model T,
modem,
monastery,
monitor,
moped,
mortar,
mortarboard,
mosque,
mosquito net,
motor scooter, scooter,
mountain bike, all-terrain bike, off-roader,
mountain tent,
mouse, computer mouse,
mousetrap,
moving van,
muzzle,
nail,
neck brace,
necklace,
nipple,
notebook, notebook computer,
obelisk,
oboe, hautboy, hautbois,
ocarina, sweet potato,
odometer, hodometer, mileometer, milometer,
oil filter,
organ, pipe organ,
oscilloscope, scope, cathode-ray oscilloscope, CRO,
overskirt,
oxcart,
oxygen mask,
packet,
paddle, boat paddle,
paddlewheel, paddle wheel,
padlock,
paintbrush,
"pajama, pyjama, pjs, jammies",
palace,
panpipe, pandean pipe, syrinx,
paper towel,
parachute, chute,
parallel bars, bars,
park bench,
parking meter,
passenger car, coach, carriage,
patio, terrace,
pay-phone, pay-station,
pedestal, plinth, footstall,
pencil box, pencil case,
pencil sharpener,
perfume, essence,
Petri dish,
photocopier,
pick, plectrum, plectron,
pickelhaube,
picket fence, paling,
pickup, pickup truck,
pier,
piggy bank, penny bank,
pill bottle,
pillow,
ping-pong ball,
pinwheel,
pirate, pirate ship,
pitcher, ewer,
"plane, carpenters plane, woodworking plane",
planetarium,
plastic bag,
plate rack,
plow, plough,
"plunger, plumbers helper",
Polaroid camera, Polaroid Land camera,
pole,
police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria,
poncho,
pool table, billiard table, snooker table,
pop bottle, soda bottle,
pot, flowerpot,
"potters wheel",
power drill,
prayer rug, prayer mat,
printer,
prison, prison house,
projectile, missile,
projector,
puck, hockey puck,
punching bag, punch bag, punching ball, punchball,
purse,
quill, quill pen,
quilt, comforter, comfort, puff,
racer, race car, racing car,
racket, racquet,
radiator,
radio, wireless,
radio telescope, radio reflector,
rain barrel,
recreational vehicle, RV, R.V.,
reel,
reflex camera,
refrigerator, icebox,
remote control, remote,
restaurant, eating house, eating place, eatery,
revolver, six-gun, six-shooter,
rifle,
rocking chair, rocker,
rotisserie,
rubber eraser, rubber, pencil eraser,
rugby ball,
rule, ruler,
running shoe,
safe,
safety pin,
saltshaker, salt shaker,
sandal,
sarong,
sax, saxophone,
scabbard,
scale, weighing machine,
school bus,
schooner,
scoreboard,
screen, CRT screen,
screw,
screwdriver,
seat belt, seatbelt,
sewing machine,
shield, buckler,
shoe shop, shoe-shop, shoe store,
shoji,
shopping basket,
shopping cart,
shovel,
shower cap,
shower curtain,
ski,
ski mask,
sleeping bag,
slide rule, slipstick,
sliding door,
slot, one-armed bandit,
snorkel,
snowmobile,
snowplow, snowplough,
soap dispenser,
soccer ball,
sock,
solar dish, solar collector, solar furnace,
sombrero,
soup bowl,
space bar,
space heater,
space shuttle,
spatula,
speedboat,
"spider web, spiders web",
spindle,
sports car, sport car,
spotlight, spot,
stage,
steam locomotive,
steel arch bridge,
steel drum,
stethoscope,
stole,
stone wall,
stopwatch, stop watch,
stove,
strainer,
streetcar, tram, tramcar, trolley, trolley car,
stretcher,
studio couch, day bed,
stupa, tope,
submarine, pigboat, sub, U-boat,
suit, suit of clothes,
sundial,
sunglass,
sunglasses, dark glasses, shades,
sunscreen, sunblock, sun blocker,
suspension bridge,
swab, swob, mop,
sweatshirt,
swimming trunks, bathing trunks,
swing,
switch, electric switch, electrical switch,
syringe,
table lamp,
tank, army tank, armored combat vehicle, armoured combat vehicle,
tape player,
teapot,
teddy, teddy bear,
television, television system,
tennis ball,
thatch, thatched roof,
theater curtain, theatre curtain,
thimble,
thresher, thrasher, threshing machine,
throne,
tile roof,
toaster,
tobacco shop, tobacconist shop, tobacconist,
toilet seat,
torch,
totem pole,
tow truck, tow car, wrecker,
toyshop,
tractor,
trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi,
tray,
trench coat,
tricycle, trike, velocipede,
trimaran,
tripod,
triumphal arch,
trolleybus, trolley coach, trackless trolley,
trombone,
tub, vat,
turnstile,
typewriter keyboard,
umbrella,
unicycle, monocycle,
upright, upright piano,
vacuum, vacuum cleaner,
vase,
vault,
velvet,
vending machine,
vestment,
viaduct,
violin, fiddle,
volleyball,
waffle iron,
wall clock,
wallet, billfold, notecase, pocketbook,
wardrobe, closet, press,
warplane, military plane,
washbasin, handbasin, washbowl, lavabo, wash-hand basin,
washer, automatic washer, washing machine,
water bottle,
water jug,
water tower,
whiskey jug,
whistle,
wig,
window screen,
window shade,
Windsor tie,
wine bottle,
wing,
wok,
wooden spoon,
wool, woolen, woollen,
worm fence, snake fence, snake-rail fence, Virginia fence,
wreck,
yawl,
yurt,
web site, website, internet site, site,
comic book,
crossword puzzle, crossword,
street sign,
traffic light, traffic signal, stoplight,
book jacket, dust cover, dust jacket, dust wrapper,
menu,
plate,
guacamole,
consomme,
hot pot, hotpot,
trifle,
ice cream, icecream,
ice lolly, lolly, lollipop, popsicle,
French loaf,
bagel, beigel,
pretzel,
cheeseburger,
hotdog, hot dog, red hot,
mashed potato,
head cabbage,
broccoli,
cauliflower,
zucchini, courgette,
spaghetti squash,
acorn squash,
butternut squash,
cucumber, cuke,
artichoke, globe artichoke,
bell pepper,
cardoon,
mushroom,
Granny Smith,
strawberry,
orange,
lemon,
fig,
pineapple, ananas,
banana,
jackfruit, jak, jack,
custard apple,
pomegranate,
hay,
carbonara,
chocolate sauce, chocolate syrup,
dough,
meat loaf, meatloaf,
pizza, pizza pie,
potpie,
burrito,
red wine,
espresso,
cup,
eggnog,
alp,
bubble,
cliff, drop, drop-off,
coral reef,
geyser,
lakeside, lakeshore,
promontory, headland, head, foreland,
sandbar, sand bar,
seashore, coast, seacoast, sea-coast,
valley, vale,
volcano,
ballplayer, baseball player,
groom, bridegroom,
scuba diver,
rapeseed,
daisy,
"yellow ladys slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",
corn,
acorn,
hip, rose hip, rosehip,
buckeye, horse chestnut, conker,
coral fungus,
agaric,
gyromitra,
stinkhorn, carrion fungus,
earthstar,
hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa,
bolete,
ear, spike, capitulum,
toilet tissue, toilet paper, bathroom tissue
python/examples/pipeline/PaddleClas/MobileNetV1/pipeline_http_client.py 0 → 100644
浏览文件 @ e9ecf97c
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')
if __name__ == "__main__":
url = "http://127.0.0.1:18080/imagenet/prediction"
with open(os.path.join(".", "daisy.jpg"), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
data = {"key": ["image"], "value": [image]}
for i in range(100):
r = requests.post(url=url, data=json.dumps(data))
print(r.json())
python/examples/pipeline/PaddleClas/MobileNetV1/pipeline_rpc_client.py 0 → 100644
浏览文件 @ e9ecf97c
# 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.pipeline import PipelineClient
except ImportError:
from paddle_serving_server.pipeline import PipelineClient
import numpy as np
import requests
import json
import cv2
import base64
import os
client = PipelineClient()
client.connect(['127.0.0.1:9993'])
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/PaddleClas/MobileNetV1/resnet50_web_service.py 0 → 100644
浏览文件 @ e9ecf97c
# 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()
batch_size = len(input_dict.keys())
imgs = []
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)
img = self.seq(im)
imgs.append(img[np.newaxis, :].copy())
input_imgs = np.concatenate(imgs, axis=0)
return {"image": input_imgs}, False, None, ""
def postprocess(self, input_dicts, fetch_dict, log_id):
score_list = fetch_dict["prediction"]
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/pipeline/PaddleClas/MobileNetV2/README.md 0 → 100644
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# 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/PaddleClas/MobileNetV2/README_CN.md 0 → 100644
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# Imagenet Pipeline WebService
这里以 Imagenet 服务为例来介绍 Pipeline WebService 的使用。
## 获取模型
```
sh get_model.sh
```
## 启动服务
```
python resnet50_web_service.py &>log.txt &
```
## 测试
```
python pipeline_rpc_client.py
```
python/examples/pipeline/PaddleClas/MobileNetV2/benchmark.py 0 → 100644
浏览文件 @ e9ecf97c
import sys
import os
import base64
import yaml
import requests
import time
import json
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 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, gpu_id):
fin = open("config.yml", "r")
config = yaml.load(fin)
fin.close()
config["dag"]["tracer"] = {"interval_s": 10}
if device == "gpu":
config["op"]["imagenet"]["local_service_conf"]["device_type"] = 1
config["op"]["imagenet"]["local_service_conf"]["devices"] = gpu_id
else:
config["op"]["imagenet"]["local_service_conf"]["device_type"] = 0
with open("config2.yml", "w") as fout:
yaml.dump(config, fout, default_flow_style=False)
def cv2_to_base64(image):
return base64.b64encode(image).decode('utf8')
def run_http(idx, batch_size):
print("start thread ({})".format(idx))
url = "http://127.0.0.1:18080/imagenet/prediction"
start = time.time()
with open(os.path.join(".", "daisy.jpg"), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
keys, values = [], []
for i in range(batch_size):
keys.append("image_{}".format(i))
values.append(image)
data = {"key": keys, "value": values}
latency_list = []
start_time = time.time()
total_num = 0
while True:
l_start = time.time()
r = requests.post(url=url, data=json.dumps(data))
print(r.json())
l_end = time.time()
latency_list.append(l_end * 1000 - l_start * 1000)
total_num += 1
if time.time() - start_time > 20:
break
end = time.time()
return [[end - start], latency_list, [total_num]]
def multithread_http(thread, batch_size):
multi_thread_runner = MultiThreadRunner()
start = time.time()
result = multi_thread_runner.run(run_http, thread, batch_size)
end = time.time()
total_cost = end - start
avg_cost = 0
total_number = 0
for i in range(thread):
avg_cost += result[0][i]
total_number += result[2][i]
avg_cost = avg_cost / thread
print("Total cost: {}s".format(total_cost))
print("Each thread cost: {}s. ".format(avg_cost))
print("Total count: {}. ".format(total_number))
print("AVG QPS: {} samples/s".format(batch_size * total_number /
total_cost))
show_latency(result[1])
def run_rpc(thread, batch_size):
client = PipelineClient()
client.connect(['127.0.0.1:18080'])
start = time.time()
test_img_dir = "imgs/"
for img_file in os.listdir(test_img_dir):
with open(os.path.join(test_img_dir, img_file), 'rb') as file:
image_data = file.read()
image = cv2_to_base64(image_data)
start_time = time.time()
while True:
ret = client.predict(feed_dict={"image": image}, fetch=["res"])
if time.time() - start_time > 10:
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]
if device == "gpu":
gpu_id = sys.argv[5]
else:
gpu_id = None
gen_yml(device, gpu_id)
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/PaddleClas/MobileNetV2/benchmark.sh 0 → 100644
浏览文件 @ e9ecf97c
export FLAGS_profile_pipeline=1
alias python3="python3.6"
modelname="clas-MobileNetV2"
# HTTP
#ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3
# Create yaml，If you already have the config.yaml, ignore it.
#python3 benchmark.py yaml local_predictor 1 gpu
rm -rf profile_log_$modelname
echo "Starting HTTP Clients..."
# Start a client in each thread, tesing the case of multiple threads.
for thread_num in 1 2 4 8 12 16
do
for batch_size in 1
do
echo "----${modelname} thread num: ${thread_num} batch size: ${batch_size} mode:http ----" >>profile_log_$modelname
# Start one web service, If you start the service yourself, you can ignore it here.
#python3 web_service.py >web.log 2>&1 &
#sleep 3
# --id is the serial number of the GPU card, Must be the same as the gpu id used by the server.
nvidia-smi --id=3 --query-gpu=memory.used --format=csv -lms 1000 > gpu_use.log 2>&1 &
nvidia-smi --id=3 --query-gpu=utilization.gpu --format=csv -lms 1000 > gpu_utilization.log 2>&1 &
echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
# Start http client
python3 benchmark.py run http $thread_num $batch_size > profile 2>&1
# Collect CPU metrics, Filter data that is zero momentarily, Record the maximum value of GPU memory and the average value of GPU utilization
python3 cpu_utilization.py >> profile_log_$modelname
grep -av '^0 %' gpu_utilization.log > gpu_utilization.log.tmp
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 -F' ' '{sum+=$1} END {print "GPU_UTILIZATION:", sum/NR, sum, NR }' gpu_utilization.log.tmp >> profile_log_$modelname
# Show profiles
python3 ../../../util/show_profile.py profile $thread_num >> profile_log_$modelname
tail -n 8 profile >> profile_log_$modelname
echo '' >> profile_log_$modelname
done
done
# Kill all nvidia-smi background task.
pkill nvidia-smi
python/examples/pipeline/PaddleClas/MobileNetV2/benchmark_config.yaml 0 → 100644
浏览文件 @ e9ecf97c
cuda_version: "10.1"
cudnn_version: "7.6"
trt_version: "6.0"
python_version: "3.7"
gcc_version: "8.2"
paddle_version: "2.0.1"
cpu: "Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz X12"
gpu: "T4"
xpu: "None"
api: ""
owner: "cuicheng01"
model_name: "MobileNetV2"
model_type: "static"
model_source: "PaddleClas"
model_url: "https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar"
batch_size: 1
num_of_samples: 1000
input_shape: "3,224,224"
runtime_device: "gpu"
ir_optim: true
enable_memory_optim: true
enable_tensorrt: false
precision: "fp32"
enable_mkldnn: false
cpu_math_library_num_threads: ""
python/examples/pipeline/PaddleClas/MobileNetV2/benchmark_config.yaml.template 0 → 100644
浏览文件 @ e9ecf97c
cuda_version: "10.1"
cudnn_version: "7.6"
trt_version: "6.0"
python_version: "3.7"
gcc_version: "8.2"
paddle_version: "2.0.1"
cpu: "Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz X12"
gpu: "T4"
xpu: "None"
api: ""
owner: "cuicheng01"
model_name: "imagenet"
model_type: "static"
model_source: "PaddleClas"
model_url: "model_url_path"
batch_size: 1
num_of_samples: 1000
input_shape: "3,224,224"
runtime_device: "cpu"
ir_optim: true
enable_memory_optim: true
enable_tensorrt: false
precision: "fp32"
enable_mkldnn: false
cpu_math_library_num_threads: ""
python/examples/pipeline/PaddleClas/MobileNetV2/benchmark_gpu.sh 0 → 100644
浏览文件 @ e9ecf97c
export FLAGS_profile_pipeline=1
alias python3="python3.7"
modelname="imagenet"
use_gpu=1
gpu_id="0"
benchmark_config_filename="benchmark_config.yaml"
# HTTP
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3
if [ $use_gpu -eq 1 ]; then
python3 benchmark.py yaml local_predictor 1 gpu $gpu_id
else
python3 benchmark.py yaml local_predictor 1 cpu
fi
rm -rf profile_log_$modelname
for thread_num in 1
do
for batch_size in 1
do
echo "#----imagenet thread num: $thread_num batch size: $batch_size mode:http use_gpu:$use_gpu----" >>profile_log_$modelname
rm -rf PipelineServingLogs
rm -rf cpu_utilization.py
python3 resnet50_web_service.py >web.log 2>&1 &
sleep 3
nvidia-smi --id=${gpu_id} --query-compute-apps=used_memory --format=csv -lms 100 > gpu_use.log 2>&1 &
nvidia-smi --id=${gpu_id} --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
python3 -m paddle_serving_server_gpu.profiler >>profile_log_$modelname
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
ps -ef | grep nvidia-smi | 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=$modelname}} END {print "GPU_MEM:", max}' gpu_use.log >> profile_log_$modelname
awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "GPU_UTIL:", max}' gpu_utilization.log >> profile_log_$modelname
cat benchmark.log >> profile_log_$modelname
python3 -m paddle_serving_server_gpu.parse_profile --benchmark_cfg $benchmark_config_filename --benchmark_log profile_log_$modelname
#rm -rf gpu_use.log gpu_utilization.log
done
done
python/examples/pipeline/PaddleClas/MobileNetV2/config.yml 0 → 100644
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#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: 18080
rpc_port: 9993
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: 1
#uci模型路径
model_config: MobileNetV2/ppcls_model/
#计算硬件类型: 空缺时由devices决定(CPU/GPU)，0=cpu, 1=gpu, 2=tensorRT, 3=arm cpu, 4=kunlun xpu
device_type: 1
#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: ["prediction"]
python/examples/pipeline/PaddleClas/MobileNetV2/cpu_utilization.py 0 → 100644
浏览文件 @ e9ecf97c
import psutil
cpu_utilization=psutil.cpu_percent(1,False)
print('CPU_UTILIZATION:', cpu_utilization)
python/examples/pipeline/PaddleClas/MobileNetV2/get_model.sh 0 → 100644
浏览文件 @ e9ecf97c
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/model/MobileNetV2.tar
tar -xf MobileNetV2.tar
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/PaddleClas/MobileNetV2/imagenet.label 0 → 100644
浏览文件 @ e9ecf97c
tench, Tinca tinca,
goldfish, Carassius auratus,
great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias,
tiger shark, Galeocerdo cuvieri,
hammerhead, hammerhead shark,
electric ray, crampfish, numbfish, torpedo,
stingray,
cock,
hen,
ostrich, Struthio camelus,
brambling, Fringilla montifringilla,
goldfinch, Carduelis carduelis,
house finch, linnet, Carpodacus mexicanus,
junco, snowbird,
indigo bunting, indigo finch, indigo bird, Passerina cyanea,
robin, American robin, Turdus migratorius,
bulbul,
jay,
magpie,
chickadee,
water ouzel, dipper,
kite,
bald eagle, American eagle, Haliaeetus leucocephalus,
vulture,
great grey owl, great gray owl, Strix nebulosa,
European fire salamander, Salamandra salamandra,
common newt, Triturus vulgaris,
eft,
spotted salamander, Ambystoma maculatum,
axolotl, mud puppy, Ambystoma mexicanum,
bullfrog, Rana catesbeiana,
tree frog, tree-frog,
tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui,
loggerhead, loggerhead turtle, Caretta caretta,
leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea,
mud turtle,
terrapin,
box turtle, box tortoise,
banded gecko,
common iguana, iguana, Iguana iguana,
American chameleon, anole, Anolis carolinensis,
whiptail, whiptail lizard,
agama,
frilled lizard, Chlamydosaurus kingi,
alligator lizard,
Gila monster, Heloderma suspectum,
green lizard, Lacerta viridis,
African chameleon, Chamaeleo chamaeleon,
Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis,
African crocodile, Nile crocodile, Crocodylus niloticus,
American alligator, Alligator mississipiensis,
triceratops,
thunder snake, worm snake, Carphophis amoenus,
ringneck snake, ring-necked snake, ring snake,
hognose snake, puff adder, sand viper,
green snake, grass snake,
king snake, kingsnake,
garter snake, grass snake,
water snake,
vine snake,
night snake, Hypsiglena torquata,
boa constrictor, Constrictor constrictor,
rock python, rock snake, Python sebae,
Indian cobra, Naja naja,
green mamba,
sea snake,
horned viper, cerastes, sand viper, horned asp, Cerastes cornutus,
diamondback, diamondback rattlesnake, Crotalus adamanteus,
sidewinder, horned rattlesnake, Crotalus cerastes,
trilobite,
harvestman, daddy longlegs, Phalangium opilio,
scorpion,
black and gold garden spider, Argiope aurantia,
barn spider, Araneus cavaticus,
garden spider, Aranea diademata,
black widow, Latrodectus mactans,
tarantula,
wolf spider, hunting spider,
tick,
centipede,
black grouse,
ptarmigan,
ruffed grouse, partridge, Bonasa umbellus,
prairie chicken, prairie grouse, prairie fowl,
peacock,
quail,
partridge,
African grey, African gray, Psittacus erithacus,
macaw,
sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita,
lorikeet,
coucal,
bee eater,
hornbill,
hummingbird,
jacamar,
toucan,
drake,
red-breasted merganser, Mergus serrator,
goose,
black swan, Cygnus atratus,
tusker,
echidna, spiny anteater, anteater,
platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus,
wallaby, brush kangaroo,
koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus,
wombat,
jellyfish,
sea anemone, anemone,
brain coral,
flatworm, platyhelminth,
nematode, nematode worm, roundworm,
conch,
snail,
slug,
sea slug, nudibranch,
chiton, coat-of-mail shell, sea cradle, polyplacophore,
chambered nautilus, pearly nautilus, nautilus,
Dungeness crab, Cancer magister,
rock crab, Cancer irroratus,
fiddler crab,
king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica,
American lobster, Northern lobster, Maine lobster, Homarus americanus,
spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish,
crayfish, crawfish, crawdad, crawdaddy,
hermit crab,
isopod,
white stork, Ciconia ciconia,
black stork, Ciconia nigra,
spoonbill,
flamingo,
little blue heron, Egretta caerulea,
American egret, great white heron, Egretta albus,
bittern,
crane,
limpkin, Aramus pictus,
European gallinule, Porphyrio porphyrio,
American coot, marsh hen, mud hen, water hen, Fulica americana,
bustard,
ruddy turnstone, Arenaria interpres,
red-backed sandpiper, dunlin, Erolia alpina,
redshank, Tringa totanus,
dowitcher,
oystercatcher, oyster catcher,
pelican,
king penguin, Aptenodytes patagonica,
albatross, mollymawk,
grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus,
killer whale, killer, orca, grampus, sea wolf, Orcinus orca,
dugong, Dugong dugon,
sea lion,
Chihuahua,
Japanese spaniel,
Maltese dog, Maltese terrier, Maltese,
Pekinese, Pekingese, Peke,
Shih-Tzu,
Blenheim spaniel,
papillon,
toy terrier,
Rhodesian ridgeback,
Afghan hound, Afghan,
basset, basset hound,
beagle,
bloodhound, sleuthhound,
bluetick,
black-and-tan coonhound,
Walker hound, Walker foxhound,
English foxhound,
redbone,
borzoi, Russian wolfhound,
Irish wolfhound,
Italian greyhound,
whippet,
Ibizan hound, Ibizan Podenco,
Norwegian elkhound, elkhound,
otterhound, otter hound,
Saluki, gazelle hound,
Scottish deerhound, deerhound,
Weimaraner,
Staffordshire bullterrier, Staffordshire bull terrier,
American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier,
Bedlington terrier,
Border terrier,
Kerry blue terrier,
Irish terrier,
Norfolk terrier,
Norwich terrier,
Yorkshire terrier,
wire-haired fox terrier,
Lakeland terrier,
Sealyham terrier, Sealyham,
Airedale, Airedale terrier,
cairn, cairn terrier,
Australian terrier,
Dandie Dinmont, Dandie Dinmont terrier,
Boston bull, Boston terrier,
miniature schnauzer,
giant schnauzer,
standard schnauzer,
Scotch terrier, Scottish terrier, Scottie,
Tibetan terrier, chrysanthemum dog,
silky terrier, Sydney silky,
soft-coated wheaten terrier,
West Highland white terrier,
Lhasa, Lhasa apso,
flat-coated retriever,
curly-coated retriever,
golden retriever,
Labrador retriever,
Chesapeake Bay retriever,
German short-haired pointer,
vizsla, Hungarian pointer,
English setter,
Irish setter, red setter,
Gordon setter,
Brittany spaniel,
clumber, clumber spaniel,
English springer, English springer spaniel,
Welsh springer spaniel,
cocker spaniel, English cocker spaniel, cocker,
Sussex spaniel,
Irish water spaniel,
kuvasz,
schipperke,
groenendael,
malinois,
briard,
kelpie,
komondor,
Old English sheepdog, bobtail,
Shetland sheepdog, Shetland sheep dog, Shetland,
collie,
Border collie,
Bouvier des Flandres, Bouviers des Flandres,
Rottweiler,
German shepherd, German shepherd dog, German police dog, alsatian,
Doberman, Doberman pinscher,
miniature pinscher,
Greater Swiss Mountain dog,
Bernese mountain dog,
Appenzeller,
EntleBucher,
boxer,
bull mastiff,
Tibetan mastiff,
French bulldog,
Great Dane,
Saint Bernard, St Bernard,
Eskimo dog, husky,
malamute, malemute, Alaskan malamute,
Siberian husky,
dalmatian, coach dog, carriage dog,
affenpinscher, monkey pinscher, monkey dog,
basenji,
pug, pug-dog,
Leonberg,
Newfoundland, Newfoundland dog,
Great Pyrenees,
Samoyed, Samoyede,
Pomeranian,
chow, chow chow,
keeshond,
Brabancon griffon,
Pembroke, Pembroke Welsh corgi,
Cardigan, Cardigan Welsh corgi,
toy poodle,
miniature poodle,
standard poodle,
Mexican hairless,
timber wolf, grey wolf, gray wolf, Canis lupus,
white wolf, Arctic wolf, Canis lupus tundrarum,
red wolf, maned wolf, Canis rufus, Canis niger,
coyote, prairie wolf, brush wolf, Canis latrans,
dingo, warrigal, warragal, Canis dingo,
dhole, Cuon alpinus,
African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus,
hyena, hyaena,
red fox, Vulpes vulpes,
kit fox, Vulpes macrotis,
Arctic fox, white fox, Alopex lagopus,
grey fox, gray fox, Urocyon cinereoargenteus,
tabby, tabby cat,
tiger cat,
Persian cat,
Siamese cat, Siamese,
Egyptian cat,
cougar, puma, catamount, mountain lion, painter, panther, Felis concolor,
lynx, catamount,
leopard, Panthera pardus,
snow leopard, ounce, Panthera uncia,
jaguar, panther, Panthera onca, Felis onca,
lion, king of beasts, Panthera leo,
tiger, Panthera tigris,
cheetah, chetah, Acinonyx jubatus,
brown bear, bruin, Ursus arctos,
American black bear, black bear, Ursus americanus, Euarctos americanus,
ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus,
sloth bear, Melursus ursinus, Ursus ursinus,
mongoose,
meerkat, mierkat,
tiger beetle,
ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle,
ground beetle, carabid beetle,
long-horned beetle, longicorn, longicorn beetle,
leaf beetle, chrysomelid,
dung beetle,
rhinoceros beetle,
weevil,
fly,
bee,
ant, emmet, pismire,
grasshopper, hopper,
cricket,
walking stick, walkingstick, stick insect,
cockroach, roach,
mantis, mantid,
cicada, cicala,
leafhopper,
lacewing, lacewing fly,
"dragonfly, darning needle, devils darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
damselfly,
admiral,
ringlet, ringlet butterfly,
monarch, monarch butterfly, milkweed butterfly, Danaus plexippus,
cabbage butterfly,
sulphur butterfly, sulfur butterfly,
lycaenid, lycaenid butterfly,
starfish, sea star,
sea urchin,
sea cucumber, holothurian,
wood rabbit, cottontail, cottontail rabbit,
hare,
Angora, Angora rabbit,
hamster,
porcupine, hedgehog,
fox squirrel, eastern fox squirrel, Sciurus niger,
marmot,
beaver,
guinea pig, Cavia cobaya,
sorrel,
zebra,
hog, pig, grunter, squealer, Sus scrofa,
wild boar, boar, Sus scrofa,
warthog,
hippopotamus, hippo, river horse, Hippopotamus amphibius,
ox,
water buffalo, water ox, Asiatic buffalo, Bubalus bubalis,
bison,
ram, tup,
bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis,
ibex, Capra ibex,
hartebeest,
impala, Aepyceros melampus,
gazelle,
Arabian camel, dromedary, Camelus dromedarius,
llama,
weasel,
mink,
polecat, fitch, foulmart, foumart, Mustela putorius,
black-footed ferret, ferret, Mustela nigripes,
otter,
skunk, polecat, wood pussy,
badger,
armadillo,
three-toed sloth, ai, Bradypus tridactylus,
orangutan, orang, orangutang, Pongo pygmaeus,
gorilla, Gorilla gorilla,
chimpanzee, chimp, Pan troglodytes,
gibbon, Hylobates lar,
siamang, Hylobates syndactylus, Symphalangus syndactylus,
guenon, guenon monkey,
patas, hussar monkey, Erythrocebus patas,
baboon,
macaque,
langur,
colobus, colobus monkey,
proboscis monkey, Nasalis larvatus,
marmoset,
capuchin, ringtail, Cebus capucinus,
howler monkey, howler,
titi, titi monkey,
spider monkey, Ateles geoffroyi,
squirrel monkey, Saimiri sciureus,
Madagascar cat, ring-tailed lemur, Lemur catta,
indri, indris, Indri indri, Indri brevicaudatus,
Indian elephant, Elephas maximus,
African elephant, Loxodonta africana,
lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens,
giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca,
barracouta, snoek,
eel,
coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch,
rock beauty, Holocanthus tricolor,
anemone fish,
sturgeon,
gar, garfish, garpike, billfish, Lepisosteus osseus,
lionfish,
puffer, pufferfish, blowfish, globefish,
abacus,
abaya,
"academic gown, academic robe, judges robe",
accordion, piano accordion, squeeze box,
acoustic guitar,
aircraft carrier, carrier, flattop, attack aircraft carrier,
airliner,
airship, dirigible,
altar,
ambulance,
amphibian, amphibious vehicle,
analog clock,
apiary, bee house,
apron,
ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin,
assault rifle, assault gun,
backpack, back pack, knapsack, packsack, rucksack, haversack,
bakery, bakeshop, bakehouse,
balance beam, beam,
balloon,
ballpoint, ballpoint pen, ballpen, Biro,
Band Aid,
banjo,
bannister, banister, balustrade, balusters, handrail,
barbell,
barber chair,
barbershop,
barn,
barometer,
barrel, cask,
barrow, garden cart, lawn cart, wheelbarrow,
baseball,
basketball,
bassinet,
bassoon,
bathing cap, swimming cap,
bath towel,
bathtub, bathing tub, bath, tub,
beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon,
beacon, lighthouse, beacon light, pharos,
beaker,
bearskin, busby, shako,
beer bottle,
beer glass,
bell cote, bell cot,
bib,
bicycle-built-for-two, tandem bicycle, tandem,
bikini, two-piece,
binder, ring-binder,
binoculars, field glasses, opera glasses,
birdhouse,
boathouse,
bobsled, bobsleigh, bob,
bolo tie, bolo, bola tie, bola,
bonnet, poke bonnet,
bookcase,
bookshop, bookstore, bookstall,
bottlecap,
bow,
bow tie, bow-tie, bowtie,
brass, memorial tablet, plaque,
brassiere, bra, bandeau,
breakwater, groin, groyne, mole, bulwark, seawall, jetty,
breastplate, aegis, egis,
broom,
bucket, pail,
buckle,
bulletproof vest,
bullet train, bullet,
butcher shop, meat market,
cab, hack, taxi, taxicab,
caldron, cauldron,
candle, taper, wax light,
cannon,
canoe,
can opener, tin opener,
cardigan,
car mirror,
carousel, carrousel, merry-go-round, roundabout, whirligig,
"carpenters kit, tool kit",
carton,
car wheel,
cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM,
cassette,
cassette player,
castle,
catamaran,
CD player,
cello, violoncello,
cellular telephone, cellular phone, cellphone, cell, mobile phone,
chain,
chainlink fence,
chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour,
chain saw, chainsaw,
chest,
chiffonier, commode,
chime, bell, gong,
china cabinet, china closet,
Christmas stocking,
church, church building,
cinema, movie theater, movie theatre, movie house, picture palace,
cleaver, meat cleaver, chopper,
cliff dwelling,
cloak,
clog, geta, patten, sabot,
cocktail shaker,
coffee mug,
coffeepot,
coil, spiral, volute, whorl, helix,
combination lock,
computer keyboard, keypad,
confectionery, confectionary, candy store,
container ship, containership, container vessel,
convertible,
corkscrew, bottle screw,
cornet, horn, trumpet, trump,
cowboy boot,
cowboy hat, ten-gallon hat,
cradle,
crane,
crash helmet,
crate,
crib, cot,
Crock Pot,
croquet ball,
crutch,
cuirass,
dam, dike, dyke,
desk,
desktop computer,
dial telephone, dial phone,
diaper, nappy, napkin,
digital clock,
digital watch,
dining table, board,
dishrag, dishcloth,
dishwasher, dish washer, dishwashing machine,
disk brake, disc brake,
dock, dockage, docking facility,
dogsled, dog sled, dog sleigh,
dome,
doormat, welcome mat,
drilling platform, offshore rig,
drum, membranophone, tympan,
drumstick,
dumbbell,
Dutch oven,
electric fan, blower,
electric guitar,
electric locomotive,
entertainment center,
envelope,
espresso maker,
face powder,
feather boa, boa,
file, file cabinet, filing cabinet,
fireboat,
fire engine, fire truck,
fire screen, fireguard,
flagpole, flagstaff,
flute, transverse flute,
folding chair,
football helmet,
forklift,
fountain,
fountain pen,
four-poster,
freight car,
French horn, horn,
frying pan, frypan, skillet,
fur coat,
garbage truck, dustcart,
gasmask, respirator, gas helmet,
gas pump, gasoline pump, petrol pump, island dispenser,
goblet,
go-kart,
golf ball,
golfcart, golf cart,
gondola,
gong, tam-tam,
gown,
grand piano, grand,
greenhouse, nursery, glasshouse,
grille, radiator grille,
grocery store, grocery, food market, market,
guillotine,
hair slide,
hair spray,
half track,
hammer,
hamper,
hand blower, blow dryer, blow drier, hair dryer, hair drier,
hand-held computer, hand-held microcomputer,
handkerchief, hankie, hanky, hankey,
hard disc, hard disk, fixed disk,
harmonica, mouth organ, harp, mouth harp,
harp,
harvester, reaper,
hatchet,
holster,
home theater, home theatre,
honeycomb,
hook, claw,
hoopskirt, crinoline,
horizontal bar, high bar,
horse cart, horse-cart,
hourglass,
iPod,
iron, smoothing iron,
"jack-o-lantern",
jean, blue jean, denim,
jeep, landrover,
jersey, T-shirt, tee shirt,
jigsaw puzzle,
jinrikisha, ricksha, rickshaw,
joystick,
kimono,
knee pad,
knot,
lab coat, laboratory coat,
ladle,
lampshade, lamp shade,
laptop, laptop computer,
lawn mower, mower,
lens cap, lens cover,
letter opener, paper knife, paperknife,
library,
lifeboat,
lighter, light, igniter, ignitor,
limousine, limo,
liner, ocean liner,
lipstick, lip rouge,
Loafer,
lotion,
loudspeaker, speaker, speaker unit, loudspeaker system, speaker system,
"loupe, jewelers loupe",
lumbermill, sawmill,
magnetic compass,
mailbag, postbag,
mailbox, letter box,
maillot,
maillot, tank suit,
manhole cover,
maraca,
marimba, xylophone,
mask,
matchstick,
maypole,
maze, labyrinth,
measuring cup,
medicine chest, medicine cabinet,
megalith, megalithic structure,
microphone, mike,
microwave, microwave oven,
military uniform,
milk can,
minibus,
miniskirt, mini,
minivan,
missile,
mitten,
mixing bowl,
mobile home, manufactured home,
Model T,
modem,
monastery,
monitor,
moped,
mortar,
mortarboard,
mosque,
mosquito net,
motor scooter, scooter,
mountain bike, all-terrain bike, off-roader,
mountain tent,
mouse, computer mouse,
mousetrap,
moving van,
muzzle,
nail,
neck brace,
necklace,
nipple,
notebook, notebook computer,
obelisk,
oboe, hautboy, hautbois,
ocarina, sweet potato,
odometer, hodometer, mileometer, milometer,
oil filter,
organ, pipe organ,
oscilloscope, scope, cathode-ray oscilloscope, CRO,
overskirt,
oxcart,
oxygen mask,
packet,
paddle, boat paddle,
paddlewheel, paddle wheel,
padlock,
paintbrush,
"pajama, pyjama, pjs, jammies",
palace,
panpipe, pandean pipe, syrinx,
paper towel,
parachute, chute,
parallel bars, bars,
park bench,
parking meter,
passenger car, coach, carriage,
patio, terrace,
pay-phone, pay-station,
pedestal, plinth, footstall,
pencil box, pencil case,
pencil sharpener,
perfume, essence,
Petri dish,
photocopier,
pick, plectrum, plectron,
pickelhaube,
picket fence, paling,
pickup, pickup truck,
pier,
piggy bank, penny bank,
pill bottle,
pillow,
ping-pong ball,
pinwheel,
pirate, pirate ship,
pitcher, ewer,
"plane, carpenters plane, woodworking plane",
planetarium,
plastic bag,
plate rack,
plow, plough,
"plunger, plumbers helper",
Polaroid camera, Polaroid Land camera,
pole,
police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria,
poncho,
pool table, billiard table, snooker table,
pop bottle, soda bottle,
pot, flowerpot,
"potters wheel",
power drill,
prayer rug, prayer mat,
printer,
prison, prison house,
projectile, missile,
projector,
puck, hockey puck,
punching bag, punch bag, punching ball, punchball,
purse,
quill, quill pen,
quilt, comforter, comfort, puff,
racer, race car, racing car,
racket, racquet,
radiator,
radio, wireless,
radio telescope, radio reflector,
rain barrel,
recreational vehicle, RV, R.V.,
reel,
reflex camera,
refrigerator, icebox,
remote control, remote,
restaurant, eating house, eating place, eatery,
revolver, six-gun, six-shooter,
rifle,
rocking chair, rocker,
rotisserie,
rubber eraser, rubber, pencil eraser,
rugby ball,
rule, ruler,
running shoe,
safe,
safety pin,
saltshaker, salt shaker,
sandal,
sarong,
sax, saxophone,
scabbard,
scale, weighing machine,
school bus,
schooner,
scoreboard,
screen, CRT screen,
screw,
screwdriver,
seat belt, seatbelt,
sewing machine,
shield, buckler,
shoe shop, shoe-shop, shoe store,
shoji,
shopping basket,
shopping cart,
shovel,
shower cap,
shower curtain,
ski,
ski mask,
sleeping bag,
slide rule, slipstick,
sliding door,
slot, one-armed bandit,
snorkel,
snowmobile,
snowplow, snowplough,
soap dispenser,
soccer ball,
sock,
solar dish, solar collector, solar furnace,
sombrero,
soup bowl,
space bar,
space heater,
space shuttle,
spatula,
speedboat,
"spider web, spiders web",
spindle,
sports car, sport car,
spotlight, spot,
stage,
steam locomotive,
steel arch bridge,
steel drum,
stethoscope,
stole,
stone wall,
stopwatch, stop watch,
stove,
strainer,
streetcar, tram, tramcar, trolley, trolley car,
stretcher,
studio couch, day bed,
stupa, tope,
submarine, pigboat, sub, U-boat,
suit, suit of clothes,
sundial,
sunglass,
sunglasses, dark glasses, shades,
sunscreen, sunblock, sun blocker,
suspension bridge,
swab, swob, mop,
sweatshirt,
swimming trunks, bathing trunks,
swing,
switch, electric switch, electrical switch,
syringe,
table lamp,
tank, army tank, armored combat vehicle, armoured combat vehicle,
tape player,
teapot,
teddy, teddy bear,
television, television system,
tennis ball,
thatch, thatched roof,
theater curtain, theatre curtain,
thimble,
thresher, thrasher, threshing machine,
throne,
tile roof,
toaster,
tobacco shop, tobacconist shop, tobacconist,
toilet seat,
torch,
totem pole,
tow truck, tow car, wrecker,
toyshop,
tractor,
trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi,
tray,
trench coat,
tricycle, trike, velocipede,
trimaran,
tripod,
triumphal arch,
trolleybus, trolley coach, trackless trolley,
trombone,
tub, vat,
turnstile,
typewriter keyboard,
umbrella,
unicycle, monocycle,
upright, upright piano,
vacuum, vacuum cleaner,
vase,
vault,
velvet,
vending machine,
vestment,
viaduct,
violin, fiddle,
volleyball,
waffle iron,
wall clock,
wallet, billfold, notecase, pocketbook,
wardrobe, closet, press,
warplane, military plane,
washbasin, handbasin, washbowl, lavabo, wash-hand basin,
washer, automatic washer, washing machine,
water bottle,
water jug,
water tower,
whiskey jug,
whistle,
wig,
window screen,
window shade,
Windsor tie,
wine bottle,
wing,
wok,
wooden spoon,
wool, woolen, woollen,
worm fence, snake fence, snake-rail fence, Virginia fence,
wreck,
yawl,
yurt,
web site, website, internet site, site,
comic book,
crossword puzzle, crossword,
street sign,
traffic light, traffic signal, stoplight,
book jacket, dust cover, dust jacket, dust wrapper,
menu,
plate,
guacamole,
consomme,
hot pot, hotpot,
trifle,
ice cream, icecream,
ice lolly, lolly, lollipop, popsicle,
French loaf,
bagel, beigel,
pretzel,
cheeseburger,
hotdog, hot dog, red hot,
mashed potato,
head cabbage,
broccoli,
cauliflower,
zucchini, courgette,
spaghetti squash,
acorn squash,
butternut squash,
cucumber, cuke,
artichoke, globe artichoke,
bell pepper,
cardoon,
mushroom,
Granny Smith,
strawberry,
orange,
lemon,
fig,
pineapple, ananas,
banana,
jackfruit, jak, jack,
custard apple,
pomegranate,
hay,
carbonara,
chocolate sauce, chocolate syrup,
dough,
meat loaf, meatloaf,
pizza, pizza pie,
potpie,
burrito,
red wine,
espresso,
cup,
eggnog,
alp,
bubble,
cliff, drop, drop-off,
coral reef,
geyser,
lakeside, lakeshore,
promontory, headland, head, foreland,
sandbar, sand bar,
seashore, coast, seacoast, sea-coast,
valley, vale,
volcano,
ballplayer, baseball player,
groom, bridegroom,
scuba diver,
rapeseed,
daisy,
"yellow ladys slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",
corn,
acorn,
hip, rose hip, rosehip,
buckeye, horse chestnut, conker,
coral fungus,
agaric,
gyromitra,
stinkhorn, carrion fungus,
earthstar,
hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa,
bolete,
ear, spike, capitulum,
toilet tissue, toilet paper, bathroom tissue
python/examples/pipeline/PaddleClas/MobileNetV2/pipeline_http_client.py 0 → 100644
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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')
if __name__ == "__main__":
url = "http://127.0.0.1:18080/imagenet/prediction"
with open(os.path.join(".", "daisy.jpg"), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
data = {"key": ["image"], "value": [image]}
for i in range(100):
r = requests.post(url=url, data=json.dumps(data))
print(r.json())
python/examples/pipeline/PaddleClas/MobileNetV2/pipeline_rpc_client.py 0 → 100644
浏览文件 @ e9ecf97c
# 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.pipeline import PipelineClient
except ImportError:
from paddle_serving_server.pipeline import PipelineClient
import numpy as np
import requests
import json
import cv2
import base64
import os
client = PipelineClient()
client.connect(['127.0.0.1:9993'])
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/PaddleClas/MobileNetV2/resnet50_web_service.py 0 → 100644
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# 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()
batch_size = len(input_dict.keys())
imgs = []
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)
img = self.seq(im)
imgs.append(img[np.newaxis, :].copy())
input_imgs = np.concatenate(imgs, axis=0)
return {"image": input_imgs}, False, None, ""
def postprocess(self, input_dicts, fetch_dict, log_id):
score_list = fetch_dict["prediction"]
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/pipeline/PaddleClas/MobileNetV3_large_x1_0/README.md 0 → 100644
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# 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/PaddleClas/MobileNetV3_large_x1_0/README_CN.md 0 → 100644
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# Imagenet Pipeline WebService
这里以 Imagenet 服务为例来介绍 Pipeline WebService 的使用。
## 获取模型
```
sh get_model.sh
```
## 启动服务
```
python resnet50_web_service.py &>log.txt &
```
## 测试
```
python pipeline_rpc_client.py
```
python/examples/pipeline/PaddleClas/MobileNetV3_large_x1_0/benchmark.py 0 → 100644
浏览文件 @ e9ecf97c
import sys
import os
import base64
import yaml
import requests
import time
import json
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 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, gpu_id):
fin = open("config.yml", "r")
config = yaml.load(fin)
fin.close()
config["dag"]["tracer"] = {"interval_s": 10}
if device == "gpu":
config["op"]["imagenet"]["local_service_conf"]["device_type"] = 1
config["op"]["imagenet"]["local_service_conf"]["devices"] = gpu_id
else:
config["op"]["imagenet"]["local_service_conf"]["device_type"] = 0
with open("config2.yml", "w") as fout:
yaml.dump(config, fout, default_flow_style=False)
def cv2_to_base64(image):
return base64.b64encode(image).decode('utf8')
def run_http(idx, batch_size):
print("start thread ({})".format(idx))
url = "http://127.0.0.1:18080/imagenet/prediction"
start = time.time()
with open(os.path.join(".", "daisy.jpg"), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
keys, values = [], []
for i in range(batch_size):
keys.append("image_{}".format(i))
values.append(image)
data = {"key": keys, "value": values}
latency_list = []
start_time = time.time()
total_num = 0
while True:
l_start = time.time()
r = requests.post(url=url, data=json.dumps(data))
print(r.json())
l_end = time.time()
latency_list.append(l_end * 1000 - l_start * 1000)
total_num += 1
if time.time() - start_time > 20:
break
end = time.time()
return [[end - start], latency_list, [total_num]]
def multithread_http(thread, batch_size):
multi_thread_runner = MultiThreadRunner()
start = time.time()
result = multi_thread_runner.run(run_http, thread, batch_size)
end = time.time()
total_cost = end - start
avg_cost = 0
total_number = 0
for i in range(thread):
avg_cost += result[0][i]
total_number += result[2][i]
avg_cost = avg_cost / thread
print("Total cost: {}s".format(total_cost))
print("Each thread cost: {}s. ".format(avg_cost))
print("Total count: {}. ".format(total_number))
print("AVG QPS: {} samples/s".format(batch_size * total_number /
total_cost))
show_latency(result[1])
def run_rpc(thread, batch_size):
client = PipelineClient()
client.connect(['127.0.0.1:18080'])
start = time.time()
test_img_dir = "imgs/"
for img_file in os.listdir(test_img_dir):
with open(os.path.join(test_img_dir, img_file), 'rb') as file:
image_data = file.read()
image = cv2_to_base64(image_data)
start_time = time.time()
while True:
ret = client.predict(feed_dict={"image": image}, fetch=["res"])
if time.time() - start_time > 10:
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]
if device == "gpu":
gpu_id = sys.argv[5]
else:
gpu_id = None
gen_yml(device, gpu_id)
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/PaddleClas/MobileNetV3_large_x1_0/benchmark.sh 0 → 100644
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export FLAGS_profile_pipeline=1
alias python3="python3.6"
modelname="clas-MobileNetV3_large_x1_0"
# HTTP
#ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3
# Create yaml，If you already have the config.yaml, ignore it.
#python3 benchmark.py yaml local_predictor 1 gpu
rm -rf profile_log_$modelname
echo "Starting HTTP Clients..."
# Start a client in each thread, tesing the case of multiple threads.
for thread_num in 1 2 4 8 12 16
do
for batch_size in 1
do
echo "----${modelname} thread num: ${thread_num} batch size: ${batch_size} mode:http ----" >>profile_log_$modelname
# Start one web service, If you start the service yourself, you can ignore it here.
#python3 web_service.py >web.log 2>&1 &
#sleep 3
# --id is the serial number of the GPU card, Must be the same as the gpu id used by the server.
nvidia-smi --id=3 --query-gpu=memory.used --format=csv -lms 1000 > gpu_use.log 2>&1 &
nvidia-smi --id=3 --query-gpu=utilization.gpu --format=csv -lms 1000 > gpu_utilization.log 2>&1 &
echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
# Start http client
python3 benchmark.py run http $thread_num $batch_size > profile 2>&1
# Collect CPU metrics, Filter data that is zero momentarily, Record the maximum value of GPU memory and the average value of GPU utilization
python3 cpu_utilization.py >> profile_log_$modelname
grep -av '^0 %' gpu_utilization.log > gpu_utilization.log.tmp
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 -F' ' '{sum+=$1} END {print "GPU_UTILIZATION:", sum/NR, sum, NR }' gpu_utilization.log.tmp >> profile_log_$modelname
# Show profiles
python3 ../../../util/show_profile.py profile $thread_num >> profile_log_$modelname
tail -n 8 profile >> profile_log_$modelname
echo '' >> profile_log_$modelname
done
done
# Kill all nvidia-smi background task.
pkill nvidia-smi
python/examples/pipeline/PaddleClas/MobileNetV3_large_x1_0/benchmark_config.yaml 0 → 100644
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cuda_version: "10.1"
cudnn_version: "7.6"
trt_version: "6.0"
python_version: "3.7"
gcc_version: "8.2"
paddle_version: "2.0.1"
cpu: "Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz X12"
gpu: "T4"
xpu: "None"
api: ""
owner: "cuicheng01"
model_name: "MobileNetV3_large_x1_0"
model_type: "static"
model_source: "PaddleClas"
model_url: "https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_0_pretrained.tar"
batch_size: 1
num_of_samples: 1000
input_shape: "3,224,224"
runtime_device: "gpu"
ir_optim: true
enable_memory_optim: true
enable_tensorrt: false
precision: "fp32"
enable_mkldnn: false
cpu_math_library_num_threads: ""
python/examples/pipeline/PaddleClas/MobileNetV3_large_x1_0/benchmark_config.yaml.template 0 → 100644
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cuda_version: "10.1"
cudnn_version: "7.6"
trt_version: "6.0"
python_version: "3.7"
gcc_version: "8.2"
paddle_version: "2.0.1"
cpu: "Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz X12"
gpu: "T4"
xpu: "None"
api: ""
owner: "cuicheng01"
model_name: "imagenet"
model_type: "static"
model_source: "PaddleClas"
model_url: "model_url_path"
batch_size: 1
num_of_samples: 1000
input_shape: "3,224,224"
runtime_device: "cpu"
ir_optim: true
enable_memory_optim: true
enable_tensorrt: false
precision: "fp32"
enable_mkldnn: false
cpu_math_library_num_threads: ""
python/examples/pipeline/PaddleClas/MobileNetV3_large_x1_0/benchmark_gpu.sh 0 → 100644
浏览文件 @ e9ecf97c
export FLAGS_profile_pipeline=1
alias python3="python3.7"
modelname="imagenet"
use_gpu=1
gpu_id="0"
benchmark_config_filename="benchmark_config.yaml"
# HTTP
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3
if [ $use_gpu -eq 1 ]; then
python3 benchmark.py yaml local_predictor 1 gpu $gpu_id
else
python3 benchmark.py yaml local_predictor 1 cpu
fi
rm -rf profile_log_$modelname
for thread_num in 1
do
for batch_size in 1
do
echo "#----imagenet thread num: $thread_num batch size: $batch_size mode:http use_gpu:$use_gpu----" >>profile_log_$modelname
rm -rf PipelineServingLogs
rm -rf cpu_utilization.py
python3 resnet50_web_service.py >web.log 2>&1 &
sleep 3
nvidia-smi --id=${gpu_id} --query-compute-apps=used_memory --format=csv -lms 100 > gpu_use.log 2>&1 &
nvidia-smi --id=${gpu_id} --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
python3 -m paddle_serving_server_gpu.profiler >>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=$modelname}} END {print "GPU_MEM:", max}' gpu_use.log >> profile_log_$modelname
awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "GPU_UTIL:", max}' gpu_utilization.log >> profile_log_$modelname
cat benchmark.log >> profile_log_$modelname
python3 -m paddle_serving_server_gpu.parse_profile --benchmark_cfg $benchmark_config_filename --benchmark_log profile_log_$modelname
#rm -rf gpu_use.log gpu_utilization.log
done
done
python/examples/pipeline/PaddleClas/MobileNetV3_large_x1_0/config.yml 0 → 100644
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#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: 18080
rpc_port: 9993
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: MobileNetV3_large_x1_0/ppcls_model/
#计算硬件类型: 空缺时由devices决定(CPU/GPU)，0=cpu, 1=gpu, 2=tensorRT, 3=arm cpu, 4=kunlun xpu
device_type: 1
#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: ["prediction"]
python/examples/pipeline/PaddleClas/MobileNetV3_large_x1_0/cpu_utilization.py 0 → 100644
浏览文件 @ e9ecf97c
import psutil
cpu_utilization=psutil.cpu_percent(1,False)
print('CPU_UTILIZATION:', cpu_utilization)
python/examples/pipeline/PaddleClas/MobileNetV3_large_x1_0/get_model.sh 0 → 100644
浏览文件 @ e9ecf97c
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/model/MobileNetV3_large_x1_0.tar
tar -xf MobileNetV3_large_x1_0.tar
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/PaddleClas/MobileNetV3_large_x1_0/imagenet.label 0 → 100644
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tench, Tinca tinca,
goldfish, Carassius auratus,
great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias,
tiger shark, Galeocerdo cuvieri,
hammerhead, hammerhead shark,
electric ray, crampfish, numbfish, torpedo,
stingray,
cock,
hen,
ostrich, Struthio camelus,
brambling, Fringilla montifringilla,
goldfinch, Carduelis carduelis,
house finch, linnet, Carpodacus mexicanus,
junco, snowbird,
indigo bunting, indigo finch, indigo bird, Passerina cyanea,
robin, American robin, Turdus migratorius,
bulbul,
jay,
magpie,
chickadee,
water ouzel, dipper,
kite,
bald eagle, American eagle, Haliaeetus leucocephalus,
vulture,
great grey owl, great gray owl, Strix nebulosa,
European fire salamander, Salamandra salamandra,
common newt, Triturus vulgaris,
eft,
spotted salamander, Ambystoma maculatum,
axolotl, mud puppy, Ambystoma mexicanum,
bullfrog, Rana catesbeiana,
tree frog, tree-frog,
tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui,
loggerhead, loggerhead turtle, Caretta caretta,
leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea,
mud turtle,
terrapin,
box turtle, box tortoise,
banded gecko,
common iguana, iguana, Iguana iguana,
American chameleon, anole, Anolis carolinensis,
whiptail, whiptail lizard,
agama,
frilled lizard, Chlamydosaurus kingi,
alligator lizard,
Gila monster, Heloderma suspectum,
green lizard, Lacerta viridis,
African chameleon, Chamaeleo chamaeleon,
Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis,
African crocodile, Nile crocodile, Crocodylus niloticus,
American alligator, Alligator mississipiensis,
triceratops,
thunder snake, worm snake, Carphophis amoenus,
ringneck snake, ring-necked snake, ring snake,
hognose snake, puff adder, sand viper,
green snake, grass snake,
king snake, kingsnake,
garter snake, grass snake,
water snake,
vine snake,
night snake, Hypsiglena torquata,
boa constrictor, Constrictor constrictor,
rock python, rock snake, Python sebae,
Indian cobra, Naja naja,
green mamba,
sea snake,
horned viper, cerastes, sand viper, horned asp, Cerastes cornutus,
diamondback, diamondback rattlesnake, Crotalus adamanteus,
sidewinder, horned rattlesnake, Crotalus cerastes,
trilobite,
harvestman, daddy longlegs, Phalangium opilio,
scorpion,
black and gold garden spider, Argiope aurantia,
barn spider, Araneus cavaticus,
garden spider, Aranea diademata,
black widow, Latrodectus mactans,
tarantula,
wolf spider, hunting spider,
tick,
centipede,
black grouse,
ptarmigan,
ruffed grouse, partridge, Bonasa umbellus,
prairie chicken, prairie grouse, prairie fowl,
peacock,
quail,
partridge,
African grey, African gray, Psittacus erithacus,
macaw,
sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita,
lorikeet,
coucal,
bee eater,
hornbill,
hummingbird,
jacamar,
toucan,
drake,
red-breasted merganser, Mergus serrator,
goose,
black swan, Cygnus atratus,
tusker,
echidna, spiny anteater, anteater,
platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus,
wallaby, brush kangaroo,
koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus,
wombat,
jellyfish,
sea anemone, anemone,
brain coral,
flatworm, platyhelminth,
nematode, nematode worm, roundworm,
conch,
snail,
slug,
sea slug, nudibranch,
chiton, coat-of-mail shell, sea cradle, polyplacophore,
chambered nautilus, pearly nautilus, nautilus,
Dungeness crab, Cancer magister,
rock crab, Cancer irroratus,
fiddler crab,
king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica,
American lobster, Northern lobster, Maine lobster, Homarus americanus,
spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish,
crayfish, crawfish, crawdad, crawdaddy,
hermit crab,
isopod,
white stork, Ciconia ciconia,
black stork, Ciconia nigra,
spoonbill,
flamingo,
little blue heron, Egretta caerulea,
American egret, great white heron, Egretta albus,
bittern,
crane,
limpkin, Aramus pictus,
European gallinule, Porphyrio porphyrio,
American coot, marsh hen, mud hen, water hen, Fulica americana,
bustard,
ruddy turnstone, Arenaria interpres,
red-backed sandpiper, dunlin, Erolia alpina,
redshank, Tringa totanus,
dowitcher,
oystercatcher, oyster catcher,
pelican,
king penguin, Aptenodytes patagonica,
albatross, mollymawk,
grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus,
killer whale, killer, orca, grampus, sea wolf, Orcinus orca,
dugong, Dugong dugon,
sea lion,
Chihuahua,
Japanese spaniel,
Maltese dog, Maltese terrier, Maltese,
Pekinese, Pekingese, Peke,
Shih-Tzu,
Blenheim spaniel,
papillon,
toy terrier,
Rhodesian ridgeback,
Afghan hound, Afghan,
basset, basset hound,
beagle,
bloodhound, sleuthhound,
bluetick,
black-and-tan coonhound,
Walker hound, Walker foxhound,
English foxhound,
redbone,
borzoi, Russian wolfhound,
Irish wolfhound,
Italian greyhound,
whippet,
Ibizan hound, Ibizan Podenco,
Norwegian elkhound, elkhound,
otterhound, otter hound,
Saluki, gazelle hound,
Scottish deerhound, deerhound,
Weimaraner,
Staffordshire bullterrier, Staffordshire bull terrier,
American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier,
Bedlington terrier,
Border terrier,
Kerry blue terrier,
Irish terrier,
Norfolk terrier,
Norwich terrier,
Yorkshire terrier,
wire-haired fox terrier,
Lakeland terrier,
Sealyham terrier, Sealyham,
Airedale, Airedale terrier,
cairn, cairn terrier,
Australian terrier,
Dandie Dinmont, Dandie Dinmont terrier,
Boston bull, Boston terrier,
miniature schnauzer,
giant schnauzer,
standard schnauzer,
Scotch terrier, Scottish terrier, Scottie,
Tibetan terrier, chrysanthemum dog,
silky terrier, Sydney silky,
soft-coated wheaten terrier,
West Highland white terrier,
Lhasa, Lhasa apso,
flat-coated retriever,
curly-coated retriever,
golden retriever,
Labrador retriever,
Chesapeake Bay retriever,
German short-haired pointer,
vizsla, Hungarian pointer,
English setter,
Irish setter, red setter,
Gordon setter,
Brittany spaniel,
clumber, clumber spaniel,
English springer, English springer spaniel,
Welsh springer spaniel,
cocker spaniel, English cocker spaniel, cocker,
Sussex spaniel,
Irish water spaniel,
kuvasz,
schipperke,
groenendael,
malinois,
briard,
kelpie,
komondor,
Old English sheepdog, bobtail,
Shetland sheepdog, Shetland sheep dog, Shetland,
collie,
Border collie,
Bouvier des Flandres, Bouviers des Flandres,
Rottweiler,
German shepherd, German shepherd dog, German police dog, alsatian,
Doberman, Doberman pinscher,
miniature pinscher,
Greater Swiss Mountain dog,
Bernese mountain dog,
Appenzeller,
EntleBucher,
boxer,
bull mastiff,
Tibetan mastiff,
French bulldog,
Great Dane,
Saint Bernard, St Bernard,
Eskimo dog, husky,
malamute, malemute, Alaskan malamute,
Siberian husky,
dalmatian, coach dog, carriage dog,
affenpinscher, monkey pinscher, monkey dog,
basenji,
pug, pug-dog,
Leonberg,
Newfoundland, Newfoundland dog,
Great Pyrenees,
Samoyed, Samoyede,
Pomeranian,
chow, chow chow,
keeshond,
Brabancon griffon,
Pembroke, Pembroke Welsh corgi,
Cardigan, Cardigan Welsh corgi,
toy poodle,
miniature poodle,
standard poodle,
Mexican hairless,
timber wolf, grey wolf, gray wolf, Canis lupus,
white wolf, Arctic wolf, Canis lupus tundrarum,
red wolf, maned wolf, Canis rufus, Canis niger,
coyote, prairie wolf, brush wolf, Canis latrans,
dingo, warrigal, warragal, Canis dingo,
dhole, Cuon alpinus,
African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus,
hyena, hyaena,
red fox, Vulpes vulpes,
kit fox, Vulpes macrotis,
Arctic fox, white fox, Alopex lagopus,
grey fox, gray fox, Urocyon cinereoargenteus,
tabby, tabby cat,
tiger cat,
Persian cat,
Siamese cat, Siamese,
Egyptian cat,
cougar, puma, catamount, mountain lion, painter, panther, Felis concolor,
lynx, catamount,
leopard, Panthera pardus,
snow leopard, ounce, Panthera uncia,
jaguar, panther, Panthera onca, Felis onca,
lion, king of beasts, Panthera leo,
tiger, Panthera tigris,
cheetah, chetah, Acinonyx jubatus,
brown bear, bruin, Ursus arctos,
American black bear, black bear, Ursus americanus, Euarctos americanus,
ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus,
sloth bear, Melursus ursinus, Ursus ursinus,
mongoose,
meerkat, mierkat,
tiger beetle,
ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle,
ground beetle, carabid beetle,
long-horned beetle, longicorn, longicorn beetle,
leaf beetle, chrysomelid,
dung beetle,
rhinoceros beetle,
weevil,
fly,
bee,
ant, emmet, pismire,
grasshopper, hopper,
cricket,
walking stick, walkingstick, stick insect,
cockroach, roach,
mantis, mantid,
cicada, cicala,
leafhopper,
lacewing, lacewing fly,
"dragonfly, darning needle, devils darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
damselfly,
admiral,
ringlet, ringlet butterfly,
monarch, monarch butterfly, milkweed butterfly, Danaus plexippus,
cabbage butterfly,
sulphur butterfly, sulfur butterfly,
lycaenid, lycaenid butterfly,
starfish, sea star,
sea urchin,
sea cucumber, holothurian,
wood rabbit, cottontail, cottontail rabbit,
hare,
Angora, Angora rabbit,
hamster,
porcupine, hedgehog,
fox squirrel, eastern fox squirrel, Sciurus niger,
marmot,
beaver,
guinea pig, Cavia cobaya,
sorrel,
zebra,
hog, pig, grunter, squealer, Sus scrofa,
wild boar, boar, Sus scrofa,
warthog,
hippopotamus, hippo, river horse, Hippopotamus amphibius,
ox,
water buffalo, water ox, Asiatic buffalo, Bubalus bubalis,
bison,
ram, tup,
bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis,
ibex, Capra ibex,
hartebeest,
impala, Aepyceros melampus,
gazelle,
Arabian camel, dromedary, Camelus dromedarius,
llama,
weasel,
mink,
polecat, fitch, foulmart, foumart, Mustela putorius,
black-footed ferret, ferret, Mustela nigripes,
otter,
skunk, polecat, wood pussy,
badger,
armadillo,
three-toed sloth, ai, Bradypus tridactylus,
orangutan, orang, orangutang, Pongo pygmaeus,
gorilla, Gorilla gorilla,
chimpanzee, chimp, Pan troglodytes,
gibbon, Hylobates lar,
siamang, Hylobates syndactylus, Symphalangus syndactylus,
guenon, guenon monkey,
patas, hussar monkey, Erythrocebus patas,
baboon,
macaque,
langur,
colobus, colobus monkey,
proboscis monkey, Nasalis larvatus,
marmoset,
capuchin, ringtail, Cebus capucinus,
howler monkey, howler,
titi, titi monkey,
spider monkey, Ateles geoffroyi,
squirrel monkey, Saimiri sciureus,
Madagascar cat, ring-tailed lemur, Lemur catta,
indri, indris, Indri indri, Indri brevicaudatus,
Indian elephant, Elephas maximus,
African elephant, Loxodonta africana,
lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens,
giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca,
barracouta, snoek,
eel,
coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch,
rock beauty, Holocanthus tricolor,
anemone fish,
sturgeon,
gar, garfish, garpike, billfish, Lepisosteus osseus,
lionfish,
puffer, pufferfish, blowfish, globefish,
abacus,
abaya,
"academic gown, academic robe, judges robe",
accordion, piano accordion, squeeze box,
acoustic guitar,
aircraft carrier, carrier, flattop, attack aircraft carrier,
airliner,
airship, dirigible,
altar,
ambulance,
amphibian, amphibious vehicle,
analog clock,
apiary, bee house,
apron,
ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin,
assault rifle, assault gun,
backpack, back pack, knapsack, packsack, rucksack, haversack,
bakery, bakeshop, bakehouse,
balance beam, beam,
balloon,
ballpoint, ballpoint pen, ballpen, Biro,
Band Aid,
banjo,
bannister, banister, balustrade, balusters, handrail,
barbell,
barber chair,
barbershop,
barn,
barometer,
barrel, cask,
barrow, garden cart, lawn cart, wheelbarrow,
baseball,
basketball,
bassinet,
bassoon,
bathing cap, swimming cap,
bath towel,
bathtub, bathing tub, bath, tub,
beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon,
beacon, lighthouse, beacon light, pharos,
beaker,
bearskin, busby, shako,
beer bottle,
beer glass,
bell cote, bell cot,
bib,
bicycle-built-for-two, tandem bicycle, tandem,
bikini, two-piece,
binder, ring-binder,
binoculars, field glasses, opera glasses,
birdhouse,
boathouse,
bobsled, bobsleigh, bob,
bolo tie, bolo, bola tie, bola,
bonnet, poke bonnet,
bookcase,
bookshop, bookstore, bookstall,
bottlecap,
bow,
bow tie, bow-tie, bowtie,
brass, memorial tablet, plaque,
brassiere, bra, bandeau,
breakwater, groin, groyne, mole, bulwark, seawall, jetty,
breastplate, aegis, egis,
broom,
bucket, pail,
buckle,
bulletproof vest,
bullet train, bullet,
butcher shop, meat market,
cab, hack, taxi, taxicab,
caldron, cauldron,
candle, taper, wax light,
cannon,
canoe,
can opener, tin opener,
cardigan,
car mirror,
carousel, carrousel, merry-go-round, roundabout, whirligig,
"carpenters kit, tool kit",
carton,
car wheel,
cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM,
cassette,
cassette player,
castle,
catamaran,
CD player,
cello, violoncello,
cellular telephone, cellular phone, cellphone, cell, mobile phone,
chain,
chainlink fence,
chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour,
chain saw, chainsaw,
chest,
chiffonier, commode,
chime, bell, gong,
china cabinet, china closet,
Christmas stocking,
church, church building,
cinema, movie theater, movie theatre, movie house, picture palace,
cleaver, meat cleaver, chopper,
cliff dwelling,
cloak,
clog, geta, patten, sabot,
cocktail shaker,
coffee mug,
coffeepot,
coil, spiral, volute, whorl, helix,
combination lock,
computer keyboard, keypad,
confectionery, confectionary, candy store,
container ship, containership, container vessel,
convertible,
corkscrew, bottle screw,
cornet, horn, trumpet, trump,
cowboy boot,
cowboy hat, ten-gallon hat,
cradle,
crane,
crash helmet,
crate,
crib, cot,
Crock Pot,
croquet ball,
crutch,
cuirass,
dam, dike, dyke,
desk,
desktop computer,
dial telephone, dial phone,
diaper, nappy, napkin,
digital clock,
digital watch,
dining table, board,
dishrag, dishcloth,
dishwasher, dish washer, dishwashing machine,
disk brake, disc brake,
dock, dockage, docking facility,
dogsled, dog sled, dog sleigh,
dome,
doormat, welcome mat,
drilling platform, offshore rig,
drum, membranophone, tympan,
drumstick,
dumbbell,
Dutch oven,
electric fan, blower,
electric guitar,
electric locomotive,
entertainment center,
envelope,
espresso maker,
face powder,
feather boa, boa,
file, file cabinet, filing cabinet,
fireboat,
fire engine, fire truck,
fire screen, fireguard,
flagpole, flagstaff,
flute, transverse flute,
folding chair,
football helmet,
forklift,
fountain,
fountain pen,
four-poster,
freight car,
French horn, horn,
frying pan, frypan, skillet,
fur coat,
garbage truck, dustcart,
gasmask, respirator, gas helmet,
gas pump, gasoline pump, petrol pump, island dispenser,
goblet,
go-kart,
golf ball,
golfcart, golf cart,
gondola,
gong, tam-tam,
gown,
grand piano, grand,
greenhouse, nursery, glasshouse,
grille, radiator grille,
grocery store, grocery, food market, market,
guillotine,
hair slide,
hair spray,
half track,
hammer,
hamper,
hand blower, blow dryer, blow drier, hair dryer, hair drier,
hand-held computer, hand-held microcomputer,
handkerchief, hankie, hanky, hankey,
hard disc, hard disk, fixed disk,
harmonica, mouth organ, harp, mouth harp,
harp,
harvester, reaper,
hatchet,
holster,
home theater, home theatre,
honeycomb,
hook, claw,
hoopskirt, crinoline,
horizontal bar, high bar,
horse cart, horse-cart,
hourglass,
iPod,
iron, smoothing iron,
"jack-o-lantern",
jean, blue jean, denim,
jeep, landrover,
jersey, T-shirt, tee shirt,
jigsaw puzzle,
jinrikisha, ricksha, rickshaw,
joystick,
kimono,
knee pad,
knot,
lab coat, laboratory coat,
ladle,
lampshade, lamp shade,
laptop, laptop computer,
lawn mower, mower,
lens cap, lens cover,
letter opener, paper knife, paperknife,
library,
lifeboat,
lighter, light, igniter, ignitor,
limousine, limo,
liner, ocean liner,
lipstick, lip rouge,
Loafer,
lotion,
loudspeaker, speaker, speaker unit, loudspeaker system, speaker system,
"loupe, jewelers loupe",
lumbermill, sawmill,
magnetic compass,
mailbag, postbag,
mailbox, letter box,
maillot,
maillot, tank suit,
manhole cover,
maraca,
marimba, xylophone,
mask,
matchstick,
maypole,
maze, labyrinth,
measuring cup,
medicine chest, medicine cabinet,
megalith, megalithic structure,
microphone, mike,
microwave, microwave oven,
military uniform,
milk can,
minibus,
miniskirt, mini,
minivan,
missile,
mitten,
mixing bowl,
mobile home, manufactured home,
Model T,
modem,
monastery,
monitor,
moped,
mortar,
mortarboard,
mosque,
mosquito net,
motor scooter, scooter,
mountain bike, all-terrain bike, off-roader,
mountain tent,
mouse, computer mouse,
mousetrap,
moving van,
muzzle,
nail,
neck brace,
necklace,
nipple,
notebook, notebook computer,
obelisk,
oboe, hautboy, hautbois,
ocarina, sweet potato,
odometer, hodometer, mileometer, milometer,
oil filter,
organ, pipe organ,
oscilloscope, scope, cathode-ray oscilloscope, CRO,
overskirt,
oxcart,
oxygen mask,
packet,
paddle, boat paddle,
paddlewheel, paddle wheel,
padlock,
paintbrush,
"pajama, pyjama, pjs, jammies",
palace,
panpipe, pandean pipe, syrinx,
paper towel,
parachute, chute,
parallel bars, bars,
park bench,
parking meter,
passenger car, coach, carriage,
patio, terrace,
pay-phone, pay-station,
pedestal, plinth, footstall,
pencil box, pencil case,
pencil sharpener,
perfume, essence,
Petri dish,
photocopier,
pick, plectrum, plectron,
pickelhaube,
picket fence, paling,
pickup, pickup truck,
pier,
piggy bank, penny bank,
pill bottle,
pillow,
ping-pong ball,
pinwheel,
pirate, pirate ship,
pitcher, ewer,
"plane, carpenters plane, woodworking plane",
planetarium,
plastic bag,
plate rack,
plow, plough,
"plunger, plumbers helper",
Polaroid camera, Polaroid Land camera,
pole,
police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria,
poncho,
pool table, billiard table, snooker table,
pop bottle, soda bottle,
pot, flowerpot,
"potters wheel",
power drill,
prayer rug, prayer mat,
printer,
prison, prison house,
projectile, missile,
projector,
puck, hockey puck,
punching bag, punch bag, punching ball, punchball,
purse,
quill, quill pen,
quilt, comforter, comfort, puff,
racer, race car, racing car,
racket, racquet,
radiator,
radio, wireless,
radio telescope, radio reflector,
rain barrel,
recreational vehicle, RV, R.V.,
reel,
reflex camera,
refrigerator, icebox,
remote control, remote,
restaurant, eating house, eating place, eatery,
revolver, six-gun, six-shooter,
rifle,
rocking chair, rocker,
rotisserie,
rubber eraser, rubber, pencil eraser,
rugby ball,
rule, ruler,
running shoe,
safe,
safety pin,
saltshaker, salt shaker,
sandal,
sarong,
sax, saxophone,
scabbard,
scale, weighing machine,
school bus,
schooner,
scoreboard,
screen, CRT screen,
screw,
screwdriver,
seat belt, seatbelt,
sewing machine,
shield, buckler,
shoe shop, shoe-shop, shoe store,
shoji,
shopping basket,
shopping cart,
shovel,
shower cap,
shower curtain,
ski,
ski mask,
sleeping bag,
slide rule, slipstick,
sliding door,
slot, one-armed bandit,
snorkel,
snowmobile,
snowplow, snowplough,
soap dispenser,
soccer ball,
sock,
solar dish, solar collector, solar furnace,
sombrero,
soup bowl,
space bar,
space heater,
space shuttle,
spatula,
speedboat,
"spider web, spiders web",
spindle,
sports car, sport car,
spotlight, spot,
stage,
steam locomotive,
steel arch bridge,
steel drum,
stethoscope,
stole,
stone wall,
stopwatch, stop watch,
stove,
strainer,
streetcar, tram, tramcar, trolley, trolley car,
stretcher,
studio couch, day bed,
stupa, tope,
submarine, pigboat, sub, U-boat,
suit, suit of clothes,
sundial,
sunglass,
sunglasses, dark glasses, shades,
sunscreen, sunblock, sun blocker,
suspension bridge,
swab, swob, mop,
sweatshirt,
swimming trunks, bathing trunks,
swing,
switch, electric switch, electrical switch,
syringe,
table lamp,
tank, army tank, armored combat vehicle, armoured combat vehicle,
tape player,
teapot,
teddy, teddy bear,
television, television system,
tennis ball,
thatch, thatched roof,
theater curtain, theatre curtain,
thimble,
thresher, thrasher, threshing machine,
throne,
tile roof,
toaster,
tobacco shop, tobacconist shop, tobacconist,
toilet seat,
torch,
totem pole,
tow truck, tow car, wrecker,
toyshop,
tractor,
trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi,
tray,
trench coat,
tricycle, trike, velocipede,
trimaran,
tripod,
triumphal arch,
trolleybus, trolley coach, trackless trolley,
trombone,
tub, vat,
turnstile,
typewriter keyboard,
umbrella,
unicycle, monocycle,
upright, upright piano,
vacuum, vacuum cleaner,
vase,
vault,
velvet,
vending machine,
vestment,
viaduct,
violin, fiddle,
volleyball,
waffle iron,
wall clock,
wallet, billfold, notecase, pocketbook,
wardrobe, closet, press,
warplane, military plane,
washbasin, handbasin, washbowl, lavabo, wash-hand basin,
washer, automatic washer, washing machine,
water bottle,
water jug,
water tower,
whiskey jug,
whistle,
wig,
window screen,
window shade,
Windsor tie,
wine bottle,
wing,
wok,
wooden spoon,
wool, woolen, woollen,
worm fence, snake fence, snake-rail fence, Virginia fence,
wreck,
yawl,
yurt,
web site, website, internet site, site,
comic book,
crossword puzzle, crossword,
street sign,
traffic light, traffic signal, stoplight,
book jacket, dust cover, dust jacket, dust wrapper,
menu,
plate,
guacamole,
consomme,
hot pot, hotpot,
trifle,
ice cream, icecream,
ice lolly, lolly, lollipop, popsicle,
French loaf,
bagel, beigel,
pretzel,
cheeseburger,
hotdog, hot dog, red hot,
mashed potato,
head cabbage,
broccoli,
cauliflower,
zucchini, courgette,
spaghetti squash,
acorn squash,
butternut squash,
cucumber, cuke,
artichoke, globe artichoke,
bell pepper,
cardoon,
mushroom,
Granny Smith,
strawberry,
orange,
lemon,
fig,
pineapple, ananas,
banana,
jackfruit, jak, jack,
custard apple,
pomegranate,
hay,
carbonara,
chocolate sauce, chocolate syrup,
dough,
meat loaf, meatloaf,
pizza, pizza pie,
potpie,
burrito,
red wine,
espresso,
cup,
eggnog,
alp,
bubble,
cliff, drop, drop-off,
coral reef,
geyser,
lakeside, lakeshore,
promontory, headland, head, foreland,
sandbar, sand bar,
seashore, coast, seacoast, sea-coast,
valley, vale,
volcano,
ballplayer, baseball player,
groom, bridegroom,
scuba diver,
rapeseed,
daisy,
"yellow ladys slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",
corn,
acorn,
hip, rose hip, rosehip,
buckeye, horse chestnut, conker,
coral fungus,
agaric,
gyromitra,
stinkhorn, carrion fungus,
earthstar,
hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa,
bolete,
ear, spike, capitulum,
toilet tissue, toilet paper, bathroom tissue
python/examples/pipeline/PaddleClas/MobileNetV3_large_x1_0/pipeline_http_client.py 0 → 100644
浏览文件 @ e9ecf97c
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')
if __name__ == "__main__":
url = "http://127.0.0.1:18080/imagenet/prediction"
with open(os.path.join(".", "daisy.jpg"), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
data = {"key": ["image"], "value": [image]}
for i in range(100):
r = requests.post(url=url, data=json.dumps(data))
print(r.json())
python/examples/pipeline/PaddleClas/MobileNetV3_large_x1_0/pipeline_rpc_client.py 0 → 100644
浏览文件 @ e9ecf97c
# 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.pipeline import PipelineClient
except ImportError:
from paddle_serving_server.pipeline import PipelineClient
import numpy as np
import requests
import json
import cv2
import base64
import os
client = PipelineClient()
client.connect(['127.0.0.1:9993'])
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/PaddleClas/MobileNetV3_large_x1_0/resnet50_web_service.py 0 → 100644
浏览文件 @ e9ecf97c
# 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()
batch_size = len(input_dict.keys())
imgs = []
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)
img = self.seq(im)
imgs.append(img[np.newaxis, :].copy())
input_imgs = np.concatenate(imgs, axis=0)
return {"image": input_imgs}, False, None, ""
def postprocess(self, input_dicts, fetch_dict, log_id):
score_list = fetch_dict["prediction"]
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/pipeline/PaddleClas/ResNeXt101_vd_64x4d/README.md 0 → 100644
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# 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/PaddleClas/ResNeXt101_vd_64x4d/README_CN.md 0 → 100644
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# Imagenet Pipeline WebService
这里以 Imagenet 服务为例来介绍 Pipeline WebService 的使用。
## 获取模型
```
sh get_model.sh
```
## 启动服务
```
python resnet50_web_service.py &>log.txt &
```
## 测试
```
python pipeline_rpc_client.py
```
python/examples/pipeline/PaddleClas/ResNeXt101_vd_64x4d/benchmark.py 0 → 100644
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import sys
import os
import base64
import yaml
import requests
import time
import json
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 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, gpu_id):
fin = open("config.yml", "r")
config = yaml.load(fin)
fin.close()
config["dag"]["tracer"] = {"interval_s": 10}
if device == "gpu":
config["op"]["imagenet"]["local_service_conf"]["device_type"] = 1
config["op"]["imagenet"]["local_service_conf"]["devices"] = gpu_id
else:
config["op"]["imagenet"]["local_service_conf"]["device_type"] = 0
with open("config2.yml", "w") as fout:
yaml.dump(config, fout, default_flow_style=False)
def cv2_to_base64(image):
return base64.b64encode(image).decode('utf8')
def run_http(idx, batch_size):
print("start thread ({})".format(idx))
url = "http://127.0.0.1:18080/imagenet/prediction"
start = time.time()
with open(os.path.join(".", "daisy.jpg"), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
keys, values = [], []
for i in range(batch_size):
keys.append("image_{}".format(i))
values.append(image)
data = {"key": keys, "value": values}
latency_list = []
start_time = time.time()
total_num = 0
while True:
l_start = time.time()
r = requests.post(url=url, data=json.dumps(data))
print(r.json())
l_end = time.time()
latency_list.append(l_end * 1000 - l_start * 1000)
total_num += 1
if time.time() - start_time > 20:
break
end = time.time()
return [[end - start], latency_list, [total_num]]
def multithread_http(thread, batch_size):
multi_thread_runner = MultiThreadRunner()
start = time.time()
result = multi_thread_runner.run(run_http, thread, batch_size)
end = time.time()
total_cost = end - start
avg_cost = 0
total_number = 0
for i in range(thread):
avg_cost += result[0][i]
total_number += result[2][i]
avg_cost = avg_cost / thread
print("Total cost: {}s".format(total_cost))
print("Each thread cost: {}s. ".format(avg_cost))
print("Total count: {}. ".format(total_number))
print("AVG QPS: {} samples/s".format(batch_size * total_number /
total_cost))
show_latency(result[1])
def run_rpc(thread, batch_size):
client = PipelineClient()
client.connect(['127.0.0.1:18080'])
start = time.time()
test_img_dir = "imgs/"
for img_file in os.listdir(test_img_dir):
with open(os.path.join(test_img_dir, img_file), 'rb') as file:
image_data = file.read()
image = cv2_to_base64(image_data)
start_time = time.time()
while True:
ret = client.predict(feed_dict={"image": image}, fetch=["res"])
if time.time() - start_time > 10:
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]
if device == "gpu":
gpu_id = sys.argv[5]
else:
gpu_id = None
gen_yml(device, gpu_id)
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/PaddleClas/ResNeXt101_vd_64x4d/benchmark.sh 0 → 100644
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export FLAGS_profile_pipeline=1
alias python3="python3.6"
modelname="clas-ResNeXt101_vd_64x4d"
# HTTP
#ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3
# Create yaml，If you already have the config.yaml, ignore it.
#python3 benchmark.py yaml local_predictor 1 gpu
rm -rf profile_log_$modelname
echo "Starting HTTP Clients..."
# Start a client in each thread, tesing the case of multiple threads.
for thread_num in 1 2 4 8 12 16
do
for batch_size in 1
do
echo "----${modelname} thread num: ${thread_num} batch size: ${batch_size} mode:http ----" >>profile_log_$modelname
# Start one web service, If you start the service yourself, you can ignore it here.
#python3 web_service.py >web.log 2>&1 &
#sleep 3
# --id is the serial number of the GPU card, Must be the same as the gpu id used by the server.
nvidia-smi --id=3 --query-gpu=memory.used --format=csv -lms 1000 > gpu_use.log 2>&1 &
nvidia-smi --id=3 --query-gpu=utilization.gpu --format=csv -lms 1000 > gpu_utilization.log 2>&1 &
echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
# Start http client
python3 benchmark.py run http $thread_num $batch_size > profile 2>&1
# Collect CPU metrics, Filter data that is zero momentarily, Record the maximum value of GPU memory and the average value of GPU utilization
python3 cpu_utilization.py >> profile_log_$modelname
grep -av '^0 %' gpu_utilization.log > gpu_utilization.log.tmp
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 -F' ' '{sum+=$1} END {print "GPU_UTILIZATION:", sum/NR, sum, NR }' gpu_utilization.log.tmp >> profile_log_$modelname
# Show profiles
python3 ../../../util/show_profile.py profile $thread_num >> profile_log_$modelname
tail -n 8 profile >> profile_log_$modelname
echo '' >> profile_log_$modelname
done
done
# Kill all nvidia-smi background task.
pkill nvidia-smi
python/examples/pipeline/PaddleClas/ResNeXt101_vd_64x4d/benchmark_config.yaml 0 → 100644
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cuda_version: "10.1"
cudnn_version: "7.6"
trt_version: "6.0"
python_version: "3.7"
gcc_version: "8.2"
paddle_version: "2.0.1"
cpu: "Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz X12"
gpu: "T4"
xpu: "None"
api: ""
owner: "cuicheng01"
model_name: "ResNeXt101_vd_64x4d"
model_type: "static"
model_source: "PaddleClas"
model_url: "https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar"
batch_size: 1
num_of_samples: 1000
input_shape: "3,224,224"
runtime_device: "gpu"
ir_optim: true
enable_memory_optim: true
enable_tensorrt: false
precision: "fp32"
enable_mkldnn: false
cpu_math_library_num_threads: ""
python/examples/pipeline/PaddleClas/ResNeXt101_vd_64x4d/benchmark_config.yaml.template 0 → 100644
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cuda_version: "10.1"
cudnn_version: "7.6"
trt_version: "6.0"
python_version: "3.7"
gcc_version: "8.2"
paddle_version: "2.0.1"
cpu: "Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz X12"
gpu: "T4"
xpu: "None"
api: ""
owner: "cuicheng01"
model_name: "imagenet"
model_type: "static"
model_source: "PaddleClas"
model_url: "model_url_path"
batch_size: 1
num_of_samples: 1000
input_shape: "3,224,224"
runtime_device: "cpu"
ir_optim: true
enable_memory_optim: true
enable_tensorrt: false
precision: "fp32"
enable_mkldnn: false
cpu_math_library_num_threads: ""
python/examples/pipeline/PaddleClas/ResNeXt101_vd_64x4d/benchmark_gpu.sh 0 → 100644
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export FLAGS_profile_pipeline=1
alias python3="python3.7"
modelname="imagenet"
use_gpu=1
gpu_id="0"
benchmark_config_filename="benchmark_config.yaml"
# HTTP
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3
if [ $use_gpu -eq 1 ]; then
python3 benchmark.py yaml local_predictor 1 gpu $gpu_id
else
python3 benchmark.py yaml local_predictor 1 cpu
fi
rm -rf profile_log_$modelname
for thread_num in 1
do
for batch_size in 1
do
echo "#----imagenet thread num: $thread_num batch size: $batch_size mode:http use_gpu:$use_gpu----" >>profile_log_$modelname
rm -rf PipelineServingLogs
rm -rf cpu_utilization.py
python3 resnet50_web_service.py >web.log 2>&1 &
sleep 3
nvidia-smi --id=${gpu_id} --query-compute-apps=used_memory --format=csv -lms 100 > gpu_use.log 2>&1 &
nvidia-smi --id=${gpu_id} --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
python3 -m paddle_serving_server_gpu.profiler >>profile_log_$modelname
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
ps -ef | grep nvidia-smi | 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=$modelname}} END {print "GPU_MEM:", max}' gpu_use.log >> profile_log_$modelname
awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "GPU_UTIL:", max}' gpu_utilization.log >> profile_log_$modelname
cat benchmark.log >> profile_log_$modelname
python3 -m paddle_serving_server_gpu.parse_profile --benchmark_cfg $benchmark_config_filename --benchmark_log profile_log_$modelname
#rm -rf gpu_use.log gpu_utilization.log
done
done
python/examples/pipeline/PaddleClas/ResNeXt101_vd_64x4d/config.yml 0 → 100644
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#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: 18080
rpc_port: 9993
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: 1
#uci模型路径
model_config: ResNeXt101_vd_64x4d/ppcls_model/
#计算硬件类型: 空缺时由devices决定(CPU/GPU)，0=cpu, 1=gpu, 2=tensorRT, 3=arm cpu, 4=kunlun xpu
device_type: 1
#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: ["prediction"]
python/examples/pipeline/PaddleClas/ResNeXt101_vd_64x4d/cpu_utilization.py 0 → 100644
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import psutil
cpu_utilization=psutil.cpu_percent(1,False)
print('CPU_UTILIZATION:', cpu_utilization)
python/examples/pipeline/PaddleClas/ResNeXt101_vd_64x4d/get_model.sh 0 → 100644
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wget --no-check-certificate https://paddle-serving.bj.bcebos.com/model/ResNeXt101_vd_64x4d.tar
tar -xf ResNeXt101_vd_64x4d.tar
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/PaddleClas/ResNeXt101_vd_64x4d/imagenet.label 0 → 100644
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tench, Tinca tinca,
goldfish, Carassius auratus,
great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias,
tiger shark, Galeocerdo cuvieri,
hammerhead, hammerhead shark,
electric ray, crampfish, numbfish, torpedo,
stingray,
cock,
hen,
ostrich, Struthio camelus,
brambling, Fringilla montifringilla,
goldfinch, Carduelis carduelis,
house finch, linnet, Carpodacus mexicanus,
junco, snowbird,
indigo bunting, indigo finch, indigo bird, Passerina cyanea,
robin, American robin, Turdus migratorius,
bulbul,
jay,
magpie,
chickadee,
water ouzel, dipper,
kite,
bald eagle, American eagle, Haliaeetus leucocephalus,
vulture,
great grey owl, great gray owl, Strix nebulosa,
European fire salamander, Salamandra salamandra,
common newt, Triturus vulgaris,
eft,
spotted salamander, Ambystoma maculatum,
axolotl, mud puppy, Ambystoma mexicanum,
bullfrog, Rana catesbeiana,
tree frog, tree-frog,
tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui,
loggerhead, loggerhead turtle, Caretta caretta,
leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea,
mud turtle,
terrapin,
box turtle, box tortoise,
banded gecko,
common iguana, iguana, Iguana iguana,
American chameleon, anole, Anolis carolinensis,
whiptail, whiptail lizard,
agama,
frilled lizard, Chlamydosaurus kingi,
alligator lizard,
Gila monster, Heloderma suspectum,
green lizard, Lacerta viridis,
African chameleon, Chamaeleo chamaeleon,
Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis,
African crocodile, Nile crocodile, Crocodylus niloticus,
American alligator, Alligator mississipiensis,
triceratops,
thunder snake, worm snake, Carphophis amoenus,
ringneck snake, ring-necked snake, ring snake,
hognose snake, puff adder, sand viper,
green snake, grass snake,
king snake, kingsnake,
garter snake, grass snake,
water snake,
vine snake,
night snake, Hypsiglena torquata,
boa constrictor, Constrictor constrictor,
rock python, rock snake, Python sebae,
Indian cobra, Naja naja,
green mamba,
sea snake,
horned viper, cerastes, sand viper, horned asp, Cerastes cornutus,
diamondback, diamondback rattlesnake, Crotalus adamanteus,
sidewinder, horned rattlesnake, Crotalus cerastes,
trilobite,
harvestman, daddy longlegs, Phalangium opilio,
scorpion,
black and gold garden spider, Argiope aurantia,
barn spider, Araneus cavaticus,
garden spider, Aranea diademata,
black widow, Latrodectus mactans,
tarantula,
wolf spider, hunting spider,
tick,
centipede,
black grouse,
ptarmigan,
ruffed grouse, partridge, Bonasa umbellus,
prairie chicken, prairie grouse, prairie fowl,
peacock,
quail,
partridge,
African grey, African gray, Psittacus erithacus,
macaw,
sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita,
lorikeet,
coucal,
bee eater,
hornbill,
hummingbird,
jacamar,
toucan,
drake,
red-breasted merganser, Mergus serrator,
goose,
black swan, Cygnus atratus,
tusker,
echidna, spiny anteater, anteater,
platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus,
wallaby, brush kangaroo,
koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus,
wombat,
jellyfish,
sea anemone, anemone,
brain coral,
flatworm, platyhelminth,
nematode, nematode worm, roundworm,
conch,
snail,
slug,
sea slug, nudibranch,
chiton, coat-of-mail shell, sea cradle, polyplacophore,
chambered nautilus, pearly nautilus, nautilus,
Dungeness crab, Cancer magister,
rock crab, Cancer irroratus,
fiddler crab,
king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica,
American lobster, Northern lobster, Maine lobster, Homarus americanus,
spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish,
crayfish, crawfish, crawdad, crawdaddy,
hermit crab,
isopod,
white stork, Ciconia ciconia,
black stork, Ciconia nigra,
spoonbill,
flamingo,
little blue heron, Egretta caerulea,
American egret, great white heron, Egretta albus,
bittern,
crane,
limpkin, Aramus pictus,
European gallinule, Porphyrio porphyrio,
American coot, marsh hen, mud hen, water hen, Fulica americana,
bustard,
ruddy turnstone, Arenaria interpres,
red-backed sandpiper, dunlin, Erolia alpina,
redshank, Tringa totanus,
dowitcher,
oystercatcher, oyster catcher,
pelican,
king penguin, Aptenodytes patagonica,
albatross, mollymawk,
grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus,
killer whale, killer, orca, grampus, sea wolf, Orcinus orca,
dugong, Dugong dugon,
sea lion,
Chihuahua,
Japanese spaniel,
Maltese dog, Maltese terrier, Maltese,
Pekinese, Pekingese, Peke,
Shih-Tzu,
Blenheim spaniel,
papillon,
toy terrier,
Rhodesian ridgeback,
Afghan hound, Afghan,
basset, basset hound,
beagle,
bloodhound, sleuthhound,
bluetick,
black-and-tan coonhound,
Walker hound, Walker foxhound,
English foxhound,
redbone,
borzoi, Russian wolfhound,
Irish wolfhound,
Italian greyhound,
whippet,
Ibizan hound, Ibizan Podenco,
Norwegian elkhound, elkhound,
otterhound, otter hound,
Saluki, gazelle hound,
Scottish deerhound, deerhound,
Weimaraner,
Staffordshire bullterrier, Staffordshire bull terrier,
American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier,
Bedlington terrier,
Border terrier,
Kerry blue terrier,
Irish terrier,
Norfolk terrier,
Norwich terrier,
Yorkshire terrier,
wire-haired fox terrier,
Lakeland terrier,
Sealyham terrier, Sealyham,
Airedale, Airedale terrier,
cairn, cairn terrier,
Australian terrier,
Dandie Dinmont, Dandie Dinmont terrier,
Boston bull, Boston terrier,
miniature schnauzer,
giant schnauzer,
standard schnauzer,
Scotch terrier, Scottish terrier, Scottie,
Tibetan terrier, chrysanthemum dog,
silky terrier, Sydney silky,
soft-coated wheaten terrier,
West Highland white terrier,
Lhasa, Lhasa apso,
flat-coated retriever,
curly-coated retriever,
golden retriever,
Labrador retriever,
Chesapeake Bay retriever,
German short-haired pointer,
vizsla, Hungarian pointer,
English setter,
Irish setter, red setter,
Gordon setter,
Brittany spaniel,
clumber, clumber spaniel,
English springer, English springer spaniel,
Welsh springer spaniel,
cocker spaniel, English cocker spaniel, cocker,
Sussex spaniel,
Irish water spaniel,
kuvasz,
schipperke,
groenendael,
malinois,
briard,
kelpie,
komondor,
Old English sheepdog, bobtail,
Shetland sheepdog, Shetland sheep dog, Shetland,
collie,
Border collie,
Bouvier des Flandres, Bouviers des Flandres,
Rottweiler,
German shepherd, German shepherd dog, German police dog, alsatian,
Doberman, Doberman pinscher,
miniature pinscher,
Greater Swiss Mountain dog,
Bernese mountain dog,
Appenzeller,
EntleBucher,
boxer,
bull mastiff,
Tibetan mastiff,
French bulldog,
Great Dane,
Saint Bernard, St Bernard,
Eskimo dog, husky,
malamute, malemute, Alaskan malamute,
Siberian husky,
dalmatian, coach dog, carriage dog,
affenpinscher, monkey pinscher, monkey dog,
basenji,
pug, pug-dog,
Leonberg,
Newfoundland, Newfoundland dog,
Great Pyrenees,
Samoyed, Samoyede,
Pomeranian,
chow, chow chow,
keeshond,
Brabancon griffon,
Pembroke, Pembroke Welsh corgi,
Cardigan, Cardigan Welsh corgi,
toy poodle,
miniature poodle,
standard poodle,
Mexican hairless,
timber wolf, grey wolf, gray wolf, Canis lupus,
white wolf, Arctic wolf, Canis lupus tundrarum,
red wolf, maned wolf, Canis rufus, Canis niger,
coyote, prairie wolf, brush wolf, Canis latrans,
dingo, warrigal, warragal, Canis dingo,
dhole, Cuon alpinus,
African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus,
hyena, hyaena,
red fox, Vulpes vulpes,
kit fox, Vulpes macrotis,
Arctic fox, white fox, Alopex lagopus,
grey fox, gray fox, Urocyon cinereoargenteus,
tabby, tabby cat,
tiger cat,
Persian cat,
Siamese cat, Siamese,
Egyptian cat,
cougar, puma, catamount, mountain lion, painter, panther, Felis concolor,
lynx, catamount,
leopard, Panthera pardus,
snow leopard, ounce, Panthera uncia,
jaguar, panther, Panthera onca, Felis onca,
lion, king of beasts, Panthera leo,
tiger, Panthera tigris,
cheetah, chetah, Acinonyx jubatus,
brown bear, bruin, Ursus arctos,
American black bear, black bear, Ursus americanus, Euarctos americanus,
ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus,
sloth bear, Melursus ursinus, Ursus ursinus,
mongoose,
meerkat, mierkat,
tiger beetle,
ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle,
ground beetle, carabid beetle,
long-horned beetle, longicorn, longicorn beetle,
leaf beetle, chrysomelid,
dung beetle,
rhinoceros beetle,
weevil,
fly,
bee,
ant, emmet, pismire,
grasshopper, hopper,
cricket,
walking stick, walkingstick, stick insect,
cockroach, roach,
mantis, mantid,
cicada, cicala,
leafhopper,
lacewing, lacewing fly,
"dragonfly, darning needle, devils darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
damselfly,
admiral,
ringlet, ringlet butterfly,
monarch, monarch butterfly, milkweed butterfly, Danaus plexippus,
cabbage butterfly,
sulphur butterfly, sulfur butterfly,
lycaenid, lycaenid butterfly,
starfish, sea star,
sea urchin,
sea cucumber, holothurian,
wood rabbit, cottontail, cottontail rabbit,
hare,
Angora, Angora rabbit,
hamster,
porcupine, hedgehog,
fox squirrel, eastern fox squirrel, Sciurus niger,
marmot,
beaver,
guinea pig, Cavia cobaya,
sorrel,
zebra,
hog, pig, grunter, squealer, Sus scrofa,
wild boar, boar, Sus scrofa,
warthog,
hippopotamus, hippo, river horse, Hippopotamus amphibius,
ox,
water buffalo, water ox, Asiatic buffalo, Bubalus bubalis,
bison,
ram, tup,
bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis,
ibex, Capra ibex,
hartebeest,
impala, Aepyceros melampus,
gazelle,
Arabian camel, dromedary, Camelus dromedarius,
llama,
weasel,
mink,
polecat, fitch, foulmart, foumart, Mustela putorius,
black-footed ferret, ferret, Mustela nigripes,
otter,
skunk, polecat, wood pussy,
badger,
armadillo,
three-toed sloth, ai, Bradypus tridactylus,
orangutan, orang, orangutang, Pongo pygmaeus,
gorilla, Gorilla gorilla,
chimpanzee, chimp, Pan troglodytes,
gibbon, Hylobates lar,
siamang, Hylobates syndactylus, Symphalangus syndactylus,
guenon, guenon monkey,
patas, hussar monkey, Erythrocebus patas,
baboon,
macaque,
langur,
colobus, colobus monkey,
proboscis monkey, Nasalis larvatus,
marmoset,
capuchin, ringtail, Cebus capucinus,
howler monkey, howler,
titi, titi monkey,
spider monkey, Ateles geoffroyi,
squirrel monkey, Saimiri sciureus,
Madagascar cat, ring-tailed lemur, Lemur catta,
indri, indris, Indri indri, Indri brevicaudatus,
Indian elephant, Elephas maximus,
African elephant, Loxodonta africana,
lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens,
giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca,
barracouta, snoek,
eel,
coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch,
rock beauty, Holocanthus tricolor,
anemone fish,
sturgeon,
gar, garfish, garpike, billfish, Lepisosteus osseus,
lionfish,
puffer, pufferfish, blowfish, globefish,
abacus,
abaya,
"academic gown, academic robe, judges robe",
accordion, piano accordion, squeeze box,
acoustic guitar,
aircraft carrier, carrier, flattop, attack aircraft carrier,
airliner,
airship, dirigible,
altar,
ambulance,
amphibian, amphibious vehicle,
analog clock,
apiary, bee house,
apron,
ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin,
assault rifle, assault gun,
backpack, back pack, knapsack, packsack, rucksack, haversack,
bakery, bakeshop, bakehouse,
balance beam, beam,
balloon,
ballpoint, ballpoint pen, ballpen, Biro,
Band Aid,
banjo,
bannister, banister, balustrade, balusters, handrail,
barbell,
barber chair,
barbershop,
barn,
barometer,
barrel, cask,
barrow, garden cart, lawn cart, wheelbarrow,
baseball,
basketball,
bassinet,
bassoon,
bathing cap, swimming cap,
bath towel,
bathtub, bathing tub, bath, tub,
beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon,
beacon, lighthouse, beacon light, pharos,
beaker,
bearskin, busby, shako,
beer bottle,
beer glass,
bell cote, bell cot,
bib,
bicycle-built-for-two, tandem bicycle, tandem,
bikini, two-piece,
binder, ring-binder,
binoculars, field glasses, opera glasses,
birdhouse,
boathouse,
bobsled, bobsleigh, bob,
bolo tie, bolo, bola tie, bola,
bonnet, poke bonnet,
bookcase,
bookshop, bookstore, bookstall,
bottlecap,
bow,
bow tie, bow-tie, bowtie,
brass, memorial tablet, plaque,
brassiere, bra, bandeau,
breakwater, groin, groyne, mole, bulwark, seawall, jetty,
breastplate, aegis, egis,
broom,
bucket, pail,
buckle,
bulletproof vest,
bullet train, bullet,
butcher shop, meat market,
cab, hack, taxi, taxicab,
caldron, cauldron,
candle, taper, wax light,
cannon,
canoe,
can opener, tin opener,
cardigan,
car mirror,
carousel, carrousel, merry-go-round, roundabout, whirligig,
"carpenters kit, tool kit",
carton,
car wheel,
cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM,
cassette,
cassette player,
castle,
catamaran,
CD player,
cello, violoncello,
cellular telephone, cellular phone, cellphone, cell, mobile phone,
chain,
chainlink fence,
chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour,
chain saw, chainsaw,
chest,
chiffonier, commode,
chime, bell, gong,
china cabinet, china closet,
Christmas stocking,
church, church building,
cinema, movie theater, movie theatre, movie house, picture palace,
cleaver, meat cleaver, chopper,
cliff dwelling,
cloak,
clog, geta, patten, sabot,
cocktail shaker,
coffee mug,
coffeepot,
coil, spiral, volute, whorl, helix,
combination lock,
computer keyboard, keypad,
confectionery, confectionary, candy store,
container ship, containership, container vessel,
convertible,
corkscrew, bottle screw,
cornet, horn, trumpet, trump,
cowboy boot,
cowboy hat, ten-gallon hat,
cradle,
crane,
crash helmet,
crate,
crib, cot,
Crock Pot,
croquet ball,
crutch,
cuirass,
dam, dike, dyke,
desk,
desktop computer,
dial telephone, dial phone,
diaper, nappy, napkin,
digital clock,
digital watch,
dining table, board,
dishrag, dishcloth,
dishwasher, dish washer, dishwashing machine,
disk brake, disc brake,
dock, dockage, docking facility,
dogsled, dog sled, dog sleigh,
dome,
doormat, welcome mat,
drilling platform, offshore rig,
drum, membranophone, tympan,
drumstick,
dumbbell,
Dutch oven,
electric fan, blower,
electric guitar,
electric locomotive,
entertainment center,
envelope,
espresso maker,
face powder,
feather boa, boa,
file, file cabinet, filing cabinet,
fireboat,
fire engine, fire truck,
fire screen, fireguard,
flagpole, flagstaff,
flute, transverse flute,
folding chair,
football helmet,
forklift,
fountain,
fountain pen,
four-poster,
freight car,
French horn, horn,
frying pan, frypan, skillet,
fur coat,
garbage truck, dustcart,
gasmask, respirator, gas helmet,
gas pump, gasoline pump, petrol pump, island dispenser,
goblet,
go-kart,
golf ball,
golfcart, golf cart,
gondola,
gong, tam-tam,
gown,
grand piano, grand,
greenhouse, nursery, glasshouse,
grille, radiator grille,
grocery store, grocery, food market, market,
guillotine,
hair slide,
hair spray,
half track,
hammer,
hamper,
hand blower, blow dryer, blow drier, hair dryer, hair drier,
hand-held computer, hand-held microcomputer,
handkerchief, hankie, hanky, hankey,
hard disc, hard disk, fixed disk,
harmonica, mouth organ, harp, mouth harp,
harp,
harvester, reaper,
hatchet,
holster,
home theater, home theatre,
honeycomb,
hook, claw,
hoopskirt, crinoline,
horizontal bar, high bar,
horse cart, horse-cart,
hourglass,
iPod,
iron, smoothing iron,
"jack-o-lantern",
jean, blue jean, denim,
jeep, landrover,
jersey, T-shirt, tee shirt,
jigsaw puzzle,
jinrikisha, ricksha, rickshaw,
joystick,
kimono,
knee pad,
knot,
lab coat, laboratory coat,
ladle,
lampshade, lamp shade,
laptop, laptop computer,
lawn mower, mower,
lens cap, lens cover,
letter opener, paper knife, paperknife,
library,
lifeboat,
lighter, light, igniter, ignitor,
limousine, limo,
liner, ocean liner,
lipstick, lip rouge,
Loafer,
lotion,
loudspeaker, speaker, speaker unit, loudspeaker system, speaker system,
"loupe, jewelers loupe",
lumbermill, sawmill,
magnetic compass,
mailbag, postbag,
mailbox, letter box,
maillot,
maillot, tank suit,
manhole cover,
maraca,
marimba, xylophone,
mask,
matchstick,
maypole,
maze, labyrinth,
measuring cup,
medicine chest, medicine cabinet,
megalith, megalithic structure,
microphone, mike,
microwave, microwave oven,
military uniform,
milk can,
minibus,
miniskirt, mini,
minivan,
missile,
mitten,
mixing bowl,
mobile home, manufactured home,
Model T,
modem,
monastery,
monitor,
moped,
mortar,
mortarboard,
mosque,
mosquito net,
motor scooter, scooter,
mountain bike, all-terrain bike, off-roader,
mountain tent,
mouse, computer mouse,
mousetrap,
moving van,
muzzle,
nail,
neck brace,
necklace,
nipple,
notebook, notebook computer,
obelisk,
oboe, hautboy, hautbois,
ocarina, sweet potato,
odometer, hodometer, mileometer, milometer,
oil filter,
organ, pipe organ,
oscilloscope, scope, cathode-ray oscilloscope, CRO,
overskirt,
oxcart,
oxygen mask,
packet,
paddle, boat paddle,
paddlewheel, paddle wheel,
padlock,
paintbrush,
"pajama, pyjama, pjs, jammies",
palace,
panpipe, pandean pipe, syrinx,
paper towel,
parachute, chute,
parallel bars, bars,
park bench,
parking meter,
passenger car, coach, carriage,
patio, terrace,
pay-phone, pay-station,
pedestal, plinth, footstall,
pencil box, pencil case,
pencil sharpener,
perfume, essence,
Petri dish,
photocopier,
pick, plectrum, plectron,
pickelhaube,
picket fence, paling,
pickup, pickup truck,
pier,
piggy bank, penny bank,
pill bottle,
pillow,
ping-pong ball,
pinwheel,
pirate, pirate ship,
pitcher, ewer,
"plane, carpenters plane, woodworking plane",
planetarium,
plastic bag,
plate rack,
plow, plough,
"plunger, plumbers helper",
Polaroid camera, Polaroid Land camera,
pole,
police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria,
poncho,
pool table, billiard table, snooker table,
pop bottle, soda bottle,
pot, flowerpot,
"potters wheel",
power drill,
prayer rug, prayer mat,
printer,
prison, prison house,
projectile, missile,
projector,
puck, hockey puck,
punching bag, punch bag, punching ball, punchball,
purse,
quill, quill pen,
quilt, comforter, comfort, puff,
racer, race car, racing car,
racket, racquet,
radiator,
radio, wireless,
radio telescope, radio reflector,
rain barrel,
recreational vehicle, RV, R.V.,
reel,
reflex camera,
refrigerator, icebox,
remote control, remote,
restaurant, eating house, eating place, eatery,
revolver, six-gun, six-shooter,
rifle,
rocking chair, rocker,
rotisserie,
rubber eraser, rubber, pencil eraser,
rugby ball,
rule, ruler,
running shoe,
safe,
safety pin,
saltshaker, salt shaker,
sandal,
sarong,
sax, saxophone,
scabbard,
scale, weighing machine,
school bus,
schooner,
scoreboard,
screen, CRT screen,
screw,
screwdriver,
seat belt, seatbelt,
sewing machine,
shield, buckler,
shoe shop, shoe-shop, shoe store,
shoji,
shopping basket,
shopping cart,
shovel,
shower cap,
shower curtain,
ski,
ski mask,
sleeping bag,
slide rule, slipstick,
sliding door,
slot, one-armed bandit,
snorkel,
snowmobile,
snowplow, snowplough,
soap dispenser,
soccer ball,
sock,
solar dish, solar collector, solar furnace,
sombrero,
soup bowl,
space bar,
space heater,
space shuttle,
spatula,
speedboat,
"spider web, spiders web",
spindle,
sports car, sport car,
spotlight, spot,
stage,
steam locomotive,
steel arch bridge,
steel drum,
stethoscope,
stole,
stone wall,
stopwatch, stop watch,
stove,
strainer,
streetcar, tram, tramcar, trolley, trolley car,
stretcher,
studio couch, day bed,
stupa, tope,
submarine, pigboat, sub, U-boat,
suit, suit of clothes,
sundial,
sunglass,
sunglasses, dark glasses, shades,
sunscreen, sunblock, sun blocker,
suspension bridge,
swab, swob, mop,
sweatshirt,
swimming trunks, bathing trunks,
swing,
switch, electric switch, electrical switch,
syringe,
table lamp,
tank, army tank, armored combat vehicle, armoured combat vehicle,
tape player,
teapot,
teddy, teddy bear,
television, television system,
tennis ball,
thatch, thatched roof,
theater curtain, theatre curtain,
thimble,
thresher, thrasher, threshing machine,
throne,
tile roof,
toaster,
tobacco shop, tobacconist shop, tobacconist,
toilet seat,
torch,
totem pole,
tow truck, tow car, wrecker,
toyshop,
tractor,
trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi,
tray,
trench coat,
tricycle, trike, velocipede,
trimaran,
tripod,
triumphal arch,
trolleybus, trolley coach, trackless trolley,
trombone,
tub, vat,
turnstile,
typewriter keyboard,
umbrella,
unicycle, monocycle,
upright, upright piano,
vacuum, vacuum cleaner,
vase,
vault,
velvet,
vending machine,
vestment,
viaduct,
violin, fiddle,
volleyball,
waffle iron,
wall clock,
wallet, billfold, notecase, pocketbook,
wardrobe, closet, press,
warplane, military plane,
washbasin, handbasin, washbowl, lavabo, wash-hand basin,
washer, automatic washer, washing machine,
water bottle,
water jug,
water tower,
whiskey jug,
whistle,
wig,
window screen,
window shade,
Windsor tie,
wine bottle,
wing,
wok,
wooden spoon,
wool, woolen, woollen,
worm fence, snake fence, snake-rail fence, Virginia fence,
wreck,
yawl,
yurt,
web site, website, internet site, site,
comic book,
crossword puzzle, crossword,
street sign,
traffic light, traffic signal, stoplight,
book jacket, dust cover, dust jacket, dust wrapper,
menu,
plate,
guacamole,
consomme,
hot pot, hotpot,
trifle,
ice cream, icecream,
ice lolly, lolly, lollipop, popsicle,
French loaf,
bagel, beigel,
pretzel,
cheeseburger,
hotdog, hot dog, red hot,
mashed potato,
head cabbage,
broccoli,
cauliflower,
zucchini, courgette,
spaghetti squash,
acorn squash,
butternut squash,
cucumber, cuke,
artichoke, globe artichoke,
bell pepper,
cardoon,
mushroom,
Granny Smith,
strawberry,
orange,
lemon,
fig,
pineapple, ananas,
banana,
jackfruit, jak, jack,
custard apple,
pomegranate,
hay,
carbonara,
chocolate sauce, chocolate syrup,
dough,
meat loaf, meatloaf,
pizza, pizza pie,
potpie,
burrito,
red wine,
espresso,
cup,
eggnog,
alp,
bubble,
cliff, drop, drop-off,
coral reef,
geyser,
lakeside, lakeshore,
promontory, headland, head, foreland,
sandbar, sand bar,
seashore, coast, seacoast, sea-coast,
valley, vale,
volcano,
ballplayer, baseball player,
groom, bridegroom,
scuba diver,
rapeseed,
daisy,
"yellow ladys slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",
corn,
acorn,
hip, rose hip, rosehip,
buckeye, horse chestnut, conker,
coral fungus,
agaric,
gyromitra,
stinkhorn, carrion fungus,
earthstar,
hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa,
bolete,
ear, spike, capitulum,
toilet tissue, toilet paper, bathroom tissue
python/examples/pipeline/PaddleClas/ResNeXt101_vd_64x4d/pipeline_http_client.py 0 → 100644
浏览文件 @ e9ecf97c
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')
if __name__ == "__main__":
url = "http://127.0.0.1:18080/imagenet/prediction"
with open(os.path.join(".", "daisy.jpg"), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
data = {"key": ["image"], "value": [image]}
for i in range(100):
r = requests.post(url=url, data=json.dumps(data))
print(r.json())
python/examples/pipeline/PaddleClas/ResNeXt101_vd_64x4d/pipeline_rpc_client.py 0 → 100644
浏览文件 @ e9ecf97c
# 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.pipeline import PipelineClient
except ImportError:
from paddle_serving_server.pipeline import PipelineClient
import numpy as np
import requests
import json
import cv2
import base64
import os
client = PipelineClient()
client.connect(['127.0.0.1:9993'])
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/PaddleClas/ResNeXt101_vd_64x4d/resnet50_web_service.py 0 → 100644
浏览文件 @ e9ecf97c
# 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()
batch_size = len(input_dict.keys())
imgs = []
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)
img = self.seq(im)
imgs.append(img[np.newaxis, :].copy())
input_imgs = np.concatenate(imgs, axis=0)
return {"image": input_imgs}, False, None, ""
def postprocess(self, input_dicts, fetch_dict, log_id):
score_list = fetch_dict["prediction"]
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()
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# 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
```
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# Imagenet Pipeline WebService
这里以 Imagenet 服务为例来介绍 Pipeline WebService 的使用。
## 获取模型
```
sh get_model.sh
```
## 启动服务
```
python resnet50_web_service.py &>log.txt &
```
## 测试
```
python pipeline_rpc_client.py
```
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import sys
import os
import base64
import yaml
import requests
import time
import json
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 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, gpu_id):
fin = open("config.yml", "r")
config = yaml.load(fin)
fin.close()
config["dag"]["tracer"] = {"interval_s": 10}
if device == "gpu":
config["op"]["imagenet"]["local_service_conf"]["device_type"] = 1
config["op"]["imagenet"]["local_service_conf"]["devices"] = gpu_id
else:
config["op"]["imagenet"]["local_service_conf"]["device_type"] = 0
with open("config2.yml", "w") as fout:
yaml.dump(config, fout, default_flow_style=False)
def cv2_to_base64(image):
return base64.b64encode(image).decode('utf8')
def run_http(idx, batch_size):
print("start thread ({})".format(idx))
url = "http://127.0.0.1:18080/imagenet/prediction"
start = time.time()
with open(os.path.join(".", "daisy.jpg"), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
keys, values = [], []
for i in range(batch_size):
keys.append("image_{}".format(i))
values.append(image)
data = {"key": keys, "value": values}
latency_list = []
start_time = time.time()
total_num = 0
while True:
l_start = time.time()
r = requests.post(url=url, data=json.dumps(data))
print(r.json())
l_end = time.time()
latency_list.append(l_end * 1000 - l_start * 1000)
total_num += 1
if time.time() - start_time > 20:
break
end = time.time()
return [[end - start], latency_list, [total_num]]
def multithread_http(thread, batch_size):
multi_thread_runner = MultiThreadRunner()
start = time.time()
result = multi_thread_runner.run(run_http, thread, batch_size)
end = time.time()
total_cost = end - start
avg_cost = 0
total_number = 0
for i in range(thread):
avg_cost += result[0][i]
total_number += result[2][i]
avg_cost = avg_cost / thread
print("Total cost: {}s".format(total_cost))
print("Each thread cost: {}s. ".format(avg_cost))
print("Total count: {}. ".format(total_number))
print("AVG QPS: {} samples/s".format(batch_size * total_number /
total_cost))
show_latency(result[1])
def run_rpc(thread, batch_size):
client = PipelineClient()
client.connect(['127.0.0.1:18080'])
start = time.time()
test_img_dir = "imgs/"
for img_file in os.listdir(test_img_dir):
with open(os.path.join(test_img_dir, img_file), 'rb') as file:
image_data = file.read()
image = cv2_to_base64(image_data)
start_time = time.time()
while True:
ret = client.predict(feed_dict={"image": image}, fetch=["res"])
if time.time() - start_time > 10:
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]
if device == "gpu":
gpu_id = sys.argv[5]
else:
gpu_id = None
gen_yml(device, gpu_id)
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)
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export FLAGS_profile_pipeline=1
alias python3="python3.6"
modelname="clas-ResNet50_vd"
# HTTP
#ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3
# Create yaml，If you already have the config.yaml, ignore it.
#python3 benchmark.py yaml local_predictor 1 gpu
rm -rf profile_log_$modelname
echo "Starting HTTP Clients..."
# Start a client in each thread, tesing the case of multiple threads.
for thread_num in 1 2 4 8 12 16
do
for batch_size in 1
do
echo "----${modelname} thread num: ${thread_num} batch size: ${batch_size} mode:http ----" >>profile_log_$modelname
# Start one web service, If you start the service yourself, you can ignore it here.
#python3 web_service.py >web.log 2>&1 &
#sleep 3
# --id is the serial number of the GPU card, Must be the same as the gpu id used by the server.
nvidia-smi --id=3 --query-gpu=memory.used --format=csv -lms 1000 > gpu_use.log 2>&1 &
nvidia-smi --id=3 --query-gpu=utilization.gpu --format=csv -lms 1000 > gpu_utilization.log 2>&1 &
echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
# Start http client
python3 benchmark.py run http $thread_num $batch_size > profile 2>&1
# Collect CPU metrics, Filter data that is zero momentarily, Record the maximum value of GPU memory and the average value of GPU utilization
python3 cpu_utilization.py >> profile_log_$modelname
grep -av '^0 %' gpu_utilization.log > gpu_utilization.log.tmp
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 -F' ' '{sum+=$1} END {print "GPU_UTILIZATION:", sum/NR, sum, NR }' gpu_utilization.log.tmp >> profile_log_$modelname
# Show profiles
python3 ../../../util/show_profile.py profile $thread_num >> profile_log_$modelname
tail -n 8 profile >> profile_log_$modelname
echo '' >> profile_log_$modelname
done
done
# Kill all nvidia-smi background task.
pkill nvidia-smi
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cuda_version: "10.1"
cudnn_version: "7.6"
trt_version: "6.0"
python_version: "3.7"
gcc_version: "8.2"
paddle_version: "2.0.1"
cpu: "Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz X12"
gpu: "T4"
xpu: "None"
api: ""
owner: "cuicheng01"
model_name: "ResNet50_vd"
model_type: "static"
model_source: "PaddleClas"
model_url: "https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar"
batch_size: 1
num_of_samples: 1000
input_shape: "3,224,224"
runtime_device: "gpu"
ir_optim: true
enable_memory_optim: true
enable_tensorrt: false
precision: "fp32"
enable_mkldnn: false
cpu_math_library_num_threads: ""
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cuda_version: "10.1"
cudnn_version: "7.6"
trt_version: "6.0"
python_version: "3.7"
gcc_version: "8.2"
paddle_version: "2.0.1"
cpu: "Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz X12"
gpu: "T4"
xpu: "None"
api: ""
owner: "cuicheng01"
model_name: "imagenet"
model_type: "static"
model_source: "PaddleClas"
model_url: "model_url_path"
batch_size: 1
num_of_samples: 1000
input_shape: "3,224,224"
runtime_device: "cpu"
ir_optim: true
enable_memory_optim: true
enable_tensorrt: false
precision: "fp32"
enable_mkldnn: false
cpu_math_library_num_threads: ""
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export FLAGS_profile_pipeline=1
alias python3="python3.7"
modelname="imagenet"
use_gpu=1
gpu_id="0"
benchmark_config_filename="benchmark_config.yaml"
# HTTP
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3
if [ $use_gpu -eq 1 ]; then
python3 benchmark.py yaml local_predictor 1 gpu $gpu_id
else
python3 benchmark.py yaml local_predictor 1 cpu
fi
rm -rf profile_log_$modelname
for thread_num in 1
do
for batch_size in 1
do
echo "#----imagenet thread num: $thread_num batch size: $batch_size mode:http use_gpu:$use_gpu----" >>profile_log_$modelname
rm -rf PipelineServingLogs
rm -rf cpu_utilization.py
python3 resnet50_web_service.py >web.log 2>&1 &
sleep 3
nvidia-smi --id=${gpu_id} --query-compute-apps=used_memory --format=csv -lms 100 > gpu_use.log 2>&1 &
nvidia-smi --id=${gpu_id} --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
python3 -m paddle_serving_server_gpu.profiler >>profile_log_$modelname
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
ps -ef | grep nvidia-smi | 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=$modelname}} END {print "GPU_MEM:", max}' gpu_use.log >> profile_log_$modelname
awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "GPU_UTIL:", max}' gpu_utilization.log >> profile_log_$modelname
cat benchmark.log >> profile_log_$modelname
python3 -m paddle_serving_server_gpu.parse_profile --benchmark_cfg $benchmark_config_filename --benchmark_log profile_log_$modelname
#rm -rf gpu_use.log gpu_utilization.log
done
done
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#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: 18080
rpc_port: 9993
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: 1
#uci模型路径
model_config: ResNet50_vd/ppcls_model/
#计算硬件类型: 空缺时由devices决定(CPU/GPU)，0=cpu, 1=gpu, 2=tensorRT, 3=arm cpu, 4=kunlun xpu
device_type: 1
#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: ["prediction"]
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import psutil
cpu_utilization=psutil.cpu_percent(1,False)
print('CPU_UTILIZATION:', cpu_utilization)
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wget --no-check-certificate https://paddle-serving.bj.bcebos.com/model/ResNet50_vd.tar
tar -xf ResNet50_vd.tar
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/imagenet-example/image_data.tar.gz
tar -xzvf image_data.tar.gz
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tench, Tinca tinca,
goldfish, Carassius auratus,
great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias,
tiger shark, Galeocerdo cuvieri,
hammerhead, hammerhead shark,
electric ray, crampfish, numbfish, torpedo,
stingray,
cock,
hen,
ostrich, Struthio camelus,
brambling, Fringilla montifringilla,
goldfinch, Carduelis carduelis,
house finch, linnet, Carpodacus mexicanus,
junco, snowbird,
indigo bunting, indigo finch, indigo bird, Passerina cyanea,
robin, American robin, Turdus migratorius,
bulbul,
jay,
magpie,
chickadee,
water ouzel, dipper,
kite,
bald eagle, American eagle, Haliaeetus leucocephalus,
vulture,
great grey owl, great gray owl, Strix nebulosa,
European fire salamander, Salamandra salamandra,
common newt, Triturus vulgaris,
eft,
spotted salamander, Ambystoma maculatum,
axolotl, mud puppy, Ambystoma mexicanum,
bullfrog, Rana catesbeiana,
tree frog, tree-frog,
tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui,
loggerhead, loggerhead turtle, Caretta caretta,
leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea,
mud turtle,
terrapin,
box turtle, box tortoise,
banded gecko,
common iguana, iguana, Iguana iguana,
American chameleon, anole, Anolis carolinensis,
whiptail, whiptail lizard,
agama,
frilled lizard, Chlamydosaurus kingi,
alligator lizard,
Gila monster, Heloderma suspectum,
green lizard, Lacerta viridis,
African chameleon, Chamaeleo chamaeleon,
Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis,
African crocodile, Nile crocodile, Crocodylus niloticus,
American alligator, Alligator mississipiensis,
triceratops,
thunder snake, worm snake, Carphophis amoenus,
ringneck snake, ring-necked snake, ring snake,
hognose snake, puff adder, sand viper,
green snake, grass snake,
king snake, kingsnake,
garter snake, grass snake,
water snake,
vine snake,
night snake, Hypsiglena torquata,
boa constrictor, Constrictor constrictor,
rock python, rock snake, Python sebae,
Indian cobra, Naja naja,
green mamba,
sea snake,
horned viper, cerastes, sand viper, horned asp, Cerastes cornutus,
diamondback, diamondback rattlesnake, Crotalus adamanteus,
sidewinder, horned rattlesnake, Crotalus cerastes,
trilobite,
harvestman, daddy longlegs, Phalangium opilio,
scorpion,
black and gold garden spider, Argiope aurantia,
barn spider, Araneus cavaticus,
garden spider, Aranea diademata,
black widow, Latrodectus mactans,
tarantula,
wolf spider, hunting spider,
tick,
centipede,
black grouse,
ptarmigan,
ruffed grouse, partridge, Bonasa umbellus,
prairie chicken, prairie grouse, prairie fowl,
peacock,
quail,
partridge,
African grey, African gray, Psittacus erithacus,
macaw,
sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita,
lorikeet,
coucal,
bee eater,
hornbill,
hummingbird,
jacamar,
toucan,
drake,
red-breasted merganser, Mergus serrator,
goose,
black swan, Cygnus atratus,
tusker,
echidna, spiny anteater, anteater,
platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus,
wallaby, brush kangaroo,
koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus,
wombat,
jellyfish,
sea anemone, anemone,
brain coral,
flatworm, platyhelminth,
nematode, nematode worm, roundworm,
conch,
snail,
slug,
sea slug, nudibranch,
chiton, coat-of-mail shell, sea cradle, polyplacophore,
chambered nautilus, pearly nautilus, nautilus,
Dungeness crab, Cancer magister,
rock crab, Cancer irroratus,
fiddler crab,
king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica,
American lobster, Northern lobster, Maine lobster, Homarus americanus,
spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish,
crayfish, crawfish, crawdad, crawdaddy,
hermit crab,
isopod,
white stork, Ciconia ciconia,
black stork, Ciconia nigra,
spoonbill,
flamingo,
little blue heron, Egretta caerulea,
American egret, great white heron, Egretta albus,
bittern,
crane,
limpkin, Aramus pictus,
European gallinule, Porphyrio porphyrio,
American coot, marsh hen, mud hen, water hen, Fulica americana,
bustard,
ruddy turnstone, Arenaria interpres,
red-backed sandpiper, dunlin, Erolia alpina,
redshank, Tringa totanus,
dowitcher,
oystercatcher, oyster catcher,
pelican,
king penguin, Aptenodytes patagonica,
albatross, mollymawk,
grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus,
killer whale, killer, orca, grampus, sea wolf, Orcinus orca,
dugong, Dugong dugon,
sea lion,
Chihuahua,
Japanese spaniel,
Maltese dog, Maltese terrier, Maltese,
Pekinese, Pekingese, Peke,
Shih-Tzu,
Blenheim spaniel,
papillon,
toy terrier,
Rhodesian ridgeback,
Afghan hound, Afghan,
basset, basset hound,
beagle,
bloodhound, sleuthhound,
bluetick,
black-and-tan coonhound,
Walker hound, Walker foxhound,
English foxhound,
redbone,
borzoi, Russian wolfhound,
Irish wolfhound,
Italian greyhound,
whippet,
Ibizan hound, Ibizan Podenco,
Norwegian elkhound, elkhound,
otterhound, otter hound,
Saluki, gazelle hound,
Scottish deerhound, deerhound,
Weimaraner,
Staffordshire bullterrier, Staffordshire bull terrier,
American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier,
Bedlington terrier,
Border terrier,
Kerry blue terrier,
Irish terrier,
Norfolk terrier,
Norwich terrier,
Yorkshire terrier,
wire-haired fox terrier,
Lakeland terrier,
Sealyham terrier, Sealyham,
Airedale, Airedale terrier,
cairn, cairn terrier,
Australian terrier,
Dandie Dinmont, Dandie Dinmont terrier,
Boston bull, Boston terrier,
miniature schnauzer,
giant schnauzer,
standard schnauzer,
Scotch terrier, Scottish terrier, Scottie,
Tibetan terrier, chrysanthemum dog,
silky terrier, Sydney silky,
soft-coated wheaten terrier,
West Highland white terrier,
Lhasa, Lhasa apso,
flat-coated retriever,
curly-coated retriever,
golden retriever,
Labrador retriever,
Chesapeake Bay retriever,
German short-haired pointer,
vizsla, Hungarian pointer,
English setter,
Irish setter, red setter,
Gordon setter,
Brittany spaniel,
clumber, clumber spaniel,
English springer, English springer spaniel,
Welsh springer spaniel,
cocker spaniel, English cocker spaniel, cocker,
Sussex spaniel,
Irish water spaniel,
kuvasz,
schipperke,
groenendael,
malinois,
briard,
kelpie,
komondor,
Old English sheepdog, bobtail,
Shetland sheepdog, Shetland sheep dog, Shetland,
collie,
Border collie,
Bouvier des Flandres, Bouviers des Flandres,
Rottweiler,
German shepherd, German shepherd dog, German police dog, alsatian,
Doberman, Doberman pinscher,
miniature pinscher,
Greater Swiss Mountain dog,
Bernese mountain dog,
Appenzeller,
EntleBucher,
boxer,
bull mastiff,
Tibetan mastiff,
French bulldog,
Great Dane,
Saint Bernard, St Bernard,
Eskimo dog, husky,
malamute, malemute, Alaskan malamute,
Siberian husky,
dalmatian, coach dog, carriage dog,
affenpinscher, monkey pinscher, monkey dog,
basenji,
pug, pug-dog,
Leonberg,
Newfoundland, Newfoundland dog,
Great Pyrenees,
Samoyed, Samoyede,
Pomeranian,
chow, chow chow,
keeshond,
Brabancon griffon,
Pembroke, Pembroke Welsh corgi,
Cardigan, Cardigan Welsh corgi,
toy poodle,
miniature poodle,
standard poodle,
Mexican hairless,
timber wolf, grey wolf, gray wolf, Canis lupus,
white wolf, Arctic wolf, Canis lupus tundrarum,
red wolf, maned wolf, Canis rufus, Canis niger,
coyote, prairie wolf, brush wolf, Canis latrans,
dingo, warrigal, warragal, Canis dingo,
dhole, Cuon alpinus,
African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus,
hyena, hyaena,
red fox, Vulpes vulpes,
kit fox, Vulpes macrotis,
Arctic fox, white fox, Alopex lagopus,
grey fox, gray fox, Urocyon cinereoargenteus,
tabby, tabby cat,
tiger cat,
Persian cat,
Siamese cat, Siamese,
Egyptian cat,
cougar, puma, catamount, mountain lion, painter, panther, Felis concolor,
lynx, catamount,
leopard, Panthera pardus,
snow leopard, ounce, Panthera uncia,
jaguar, panther, Panthera onca, Felis onca,
lion, king of beasts, Panthera leo,
tiger, Panthera tigris,
cheetah, chetah, Acinonyx jubatus,
brown bear, bruin, Ursus arctos,
American black bear, black bear, Ursus americanus, Euarctos americanus,
ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus,
sloth bear, Melursus ursinus, Ursus ursinus,
mongoose,
meerkat, mierkat,
tiger beetle,
ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle,
ground beetle, carabid beetle,
long-horned beetle, longicorn, longicorn beetle,
leaf beetle, chrysomelid,
dung beetle,
rhinoceros beetle,
weevil,
fly,
bee,
ant, emmet, pismire,
grasshopper, hopper,
cricket,
walking stick, walkingstick, stick insect,
cockroach, roach,
mantis, mantid,
cicada, cicala,
leafhopper,
lacewing, lacewing fly,
"dragonfly, darning needle, devils darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
damselfly,
admiral,
ringlet, ringlet butterfly,
monarch, monarch butterfly, milkweed butterfly, Danaus plexippus,
cabbage butterfly,
sulphur butterfly, sulfur butterfly,
lycaenid, lycaenid butterfly,
starfish, sea star,
sea urchin,
sea cucumber, holothurian,
wood rabbit, cottontail, cottontail rabbit,
hare,
Angora, Angora rabbit,
hamster,
porcupine, hedgehog,
fox squirrel, eastern fox squirrel, Sciurus niger,
marmot,
beaver,
guinea pig, Cavia cobaya,
sorrel,
zebra,
hog, pig, grunter, squealer, Sus scrofa,
wild boar, boar, Sus scrofa,
warthog,
hippopotamus, hippo, river horse, Hippopotamus amphibius,
ox,
water buffalo, water ox, Asiatic buffalo, Bubalus bubalis,
bison,
ram, tup,
bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis,
ibex, Capra ibex,
hartebeest,
impala, Aepyceros melampus,
gazelle,
Arabian camel, dromedary, Camelus dromedarius,
llama,
weasel,
mink,
polecat, fitch, foulmart, foumart, Mustela putorius,
black-footed ferret, ferret, Mustela nigripes,
otter,
skunk, polecat, wood pussy,
badger,
armadillo,
three-toed sloth, ai, Bradypus tridactylus,
orangutan, orang, orangutang, Pongo pygmaeus,
gorilla, Gorilla gorilla,
chimpanzee, chimp, Pan troglodytes,
gibbon, Hylobates lar,
siamang, Hylobates syndactylus, Symphalangus syndactylus,
guenon, guenon monkey,
patas, hussar monkey, Erythrocebus patas,
baboon,
macaque,
langur,
colobus, colobus monkey,
proboscis monkey, Nasalis larvatus,
marmoset,
capuchin, ringtail, Cebus capucinus,
howler monkey, howler,
titi, titi monkey,
spider monkey, Ateles geoffroyi,
squirrel monkey, Saimiri sciureus,
Madagascar cat, ring-tailed lemur, Lemur catta,
indri, indris, Indri indri, Indri brevicaudatus,
Indian elephant, Elephas maximus,
African elephant, Loxodonta africana,
lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens,
giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca,
barracouta, snoek,
eel,
coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch,
rock beauty, Holocanthus tricolor,
anemone fish,
sturgeon,
gar, garfish, garpike, billfish, Lepisosteus osseus,
lionfish,
puffer, pufferfish, blowfish, globefish,
abacus,
abaya,
"academic gown, academic robe, judges robe",
accordion, piano accordion, squeeze box,
acoustic guitar,
aircraft carrier, carrier, flattop, attack aircraft carrier,
airliner,
airship, dirigible,
altar,
ambulance,
amphibian, amphibious vehicle,
analog clock,
apiary, bee house,
apron,
ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin,
assault rifle, assault gun,
backpack, back pack, knapsack, packsack, rucksack, haversack,
bakery, bakeshop, bakehouse,
balance beam, beam,
balloon,
ballpoint, ballpoint pen, ballpen, Biro,
Band Aid,
banjo,
bannister, banister, balustrade, balusters, handrail,
barbell,
barber chair,
barbershop,
barn,
barometer,
barrel, cask,
barrow, garden cart, lawn cart, wheelbarrow,
baseball,
basketball,
bassinet,
bassoon,
bathing cap, swimming cap,
bath towel,
bathtub, bathing tub, bath, tub,
beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon,
beacon, lighthouse, beacon light, pharos,
beaker,
bearskin, busby, shako,
beer bottle,
beer glass,
bell cote, bell cot,
bib,
bicycle-built-for-two, tandem bicycle, tandem,
bikini, two-piece,
binder, ring-binder,
binoculars, field glasses, opera glasses,
birdhouse,
boathouse,
bobsled, bobsleigh, bob,
bolo tie, bolo, bola tie, bola,
bonnet, poke bonnet,
bookcase,
bookshop, bookstore, bookstall,
bottlecap,
bow,
bow tie, bow-tie, bowtie,
brass, memorial tablet, plaque,
brassiere, bra, bandeau,
breakwater, groin, groyne, mole, bulwark, seawall, jetty,
breastplate, aegis, egis,
broom,
bucket, pail,
buckle,
bulletproof vest,
bullet train, bullet,
butcher shop, meat market,
cab, hack, taxi, taxicab,
caldron, cauldron,
candle, taper, wax light,
cannon,
canoe,
can opener, tin opener,
cardigan,
car mirror,
carousel, carrousel, merry-go-round, roundabout, whirligig,
"carpenters kit, tool kit",
carton,
car wheel,
cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM,
cassette,
cassette player,
castle,
catamaran,
CD player,
cello, violoncello,
cellular telephone, cellular phone, cellphone, cell, mobile phone,
chain,
chainlink fence,
chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour,
chain saw, chainsaw,
chest,
chiffonier, commode,
chime, bell, gong,
china cabinet, china closet,
Christmas stocking,
church, church building,
cinema, movie theater, movie theatre, movie house, picture palace,
cleaver, meat cleaver, chopper,
cliff dwelling,
cloak,
clog, geta, patten, sabot,
cocktail shaker,
coffee mug,
coffeepot,
coil, spiral, volute, whorl, helix,
combination lock,
computer keyboard, keypad,
confectionery, confectionary, candy store,
container ship, containership, container vessel,
convertible,
corkscrew, bottle screw,
cornet, horn, trumpet, trump,
cowboy boot,
cowboy hat, ten-gallon hat,
cradle,
crane,
crash helmet,
crate,
crib, cot,
Crock Pot,
croquet ball,
crutch,
cuirass,
dam, dike, dyke,
desk,
desktop computer,
dial telephone, dial phone,
diaper, nappy, napkin,
digital clock,
digital watch,
dining table, board,
dishrag, dishcloth,
dishwasher, dish washer, dishwashing machine,
disk brake, disc brake,
dock, dockage, docking facility,
dogsled, dog sled, dog sleigh,
dome,
doormat, welcome mat,
drilling platform, offshore rig,
drum, membranophone, tympan,
drumstick,
dumbbell,
Dutch oven,
electric fan, blower,
electric guitar,
electric locomotive,
entertainment center,
envelope,
espresso maker,
face powder,
feather boa, boa,
file, file cabinet, filing cabinet,
fireboat,
fire engine, fire truck,
fire screen, fireguard,
flagpole, flagstaff,
flute, transverse flute,
folding chair,
football helmet,
forklift,
fountain,
fountain pen,
four-poster,
freight car,
French horn, horn,
frying pan, frypan, skillet,
fur coat,
garbage truck, dustcart,
gasmask, respirator, gas helmet,
gas pump, gasoline pump, petrol pump, island dispenser,
goblet,
go-kart,
golf ball,
golfcart, golf cart,
gondola,
gong, tam-tam,
gown,
grand piano, grand,
greenhouse, nursery, glasshouse,
grille, radiator grille,
grocery store, grocery, food market, market,
guillotine,
hair slide,
hair spray,
half track,
hammer,
hamper,
hand blower, blow dryer, blow drier, hair dryer, hair drier,
hand-held computer, hand-held microcomputer,
handkerchief, hankie, hanky, hankey,
hard disc, hard disk, fixed disk,
harmonica, mouth organ, harp, mouth harp,
harp,
harvester, reaper,
hatchet,
holster,
home theater, home theatre,
honeycomb,
hook, claw,
hoopskirt, crinoline,
horizontal bar, high bar,
horse cart, horse-cart,
hourglass,
iPod,
iron, smoothing iron,
"jack-o-lantern",
jean, blue jean, denim,
jeep, landrover,
jersey, T-shirt, tee shirt,
jigsaw puzzle,
jinrikisha, ricksha, rickshaw,
joystick,
kimono,
knee pad,
knot,
lab coat, laboratory coat,
ladle,
lampshade, lamp shade,
laptop, laptop computer,
lawn mower, mower,
lens cap, lens cover,
letter opener, paper knife, paperknife,
library,
lifeboat,
lighter, light, igniter, ignitor,
limousine, limo,
liner, ocean liner,
lipstick, lip rouge,
Loafer,
lotion,
loudspeaker, speaker, speaker unit, loudspeaker system, speaker system,
"loupe, jewelers loupe",
lumbermill, sawmill,
magnetic compass,
mailbag, postbag,
mailbox, letter box,
maillot,
maillot, tank suit,
manhole cover,
maraca,
marimba, xylophone,
mask,
matchstick,
maypole,
maze, labyrinth,
measuring cup,
medicine chest, medicine cabinet,
megalith, megalithic structure,
microphone, mike,
microwave, microwave oven,
military uniform,
milk can,
minibus,
miniskirt, mini,
minivan,
missile,
mitten,
mixing bowl,
mobile home, manufactured home,
Model T,
modem,
monastery,
monitor,
moped,
mortar,
mortarboard,
mosque,
mosquito net,
motor scooter, scooter,
mountain bike, all-terrain bike, off-roader,
mountain tent,
mouse, computer mouse,
mousetrap,
moving van,
muzzle,
nail,
neck brace,
necklace,
nipple,
notebook, notebook computer,
obelisk,
oboe, hautboy, hautbois,
ocarina, sweet potato,
odometer, hodometer, mileometer, milometer,
oil filter,
organ, pipe organ,
oscilloscope, scope, cathode-ray oscilloscope, CRO,
overskirt,
oxcart,
oxygen mask,
packet,
paddle, boat paddle,
paddlewheel, paddle wheel,
padlock,
paintbrush,
"pajama, pyjama, pjs, jammies",
palace,
panpipe, pandean pipe, syrinx,
paper towel,
parachute, chute,
parallel bars, bars,
park bench,
parking meter,
passenger car, coach, carriage,
patio, terrace,
pay-phone, pay-station,
pedestal, plinth, footstall,
pencil box, pencil case,
pencil sharpener,
perfume, essence,
Petri dish,
photocopier,
pick, plectrum, plectron,
pickelhaube,
picket fence, paling,
pickup, pickup truck,
pier,
piggy bank, penny bank,
pill bottle,
pillow,
ping-pong ball,
pinwheel,
pirate, pirate ship,
pitcher, ewer,
"plane, carpenters plane, woodworking plane",
planetarium,
plastic bag,
plate rack,
plow, plough,
"plunger, plumbers helper",
Polaroid camera, Polaroid Land camera,
pole,
police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria,
poncho,
pool table, billiard table, snooker table,
pop bottle, soda bottle,
pot, flowerpot,
"potters wheel",
power drill,
prayer rug, prayer mat,
printer,
prison, prison house,
projectile, missile,
projector,
puck, hockey puck,
punching bag, punch bag, punching ball, punchball,
purse,
quill, quill pen,
quilt, comforter, comfort, puff,
racer, race car, racing car,
racket, racquet,
radiator,
radio, wireless,
radio telescope, radio reflector,
rain barrel,
recreational vehicle, RV, R.V.,
reel,
reflex camera,
refrigerator, icebox,
remote control, remote,
restaurant, eating house, eating place, eatery,
revolver, six-gun, six-shooter,
rifle,
rocking chair, rocker,
rotisserie,
rubber eraser, rubber, pencil eraser,
rugby ball,
rule, ruler,
running shoe,
safe,
safety pin,
saltshaker, salt shaker,
sandal,
sarong,
sax, saxophone,
scabbard,
scale, weighing machine,
school bus,
schooner,
scoreboard,
screen, CRT screen,
screw,
screwdriver,
seat belt, seatbelt,
sewing machine,
shield, buckler,
shoe shop, shoe-shop, shoe store,
shoji,
shopping basket,
shopping cart,
shovel,
shower cap,
shower curtain,
ski,
ski mask,
sleeping bag,
slide rule, slipstick,
sliding door,
slot, one-armed bandit,
snorkel,
snowmobile,
snowplow, snowplough,
soap dispenser,
soccer ball,
sock,
solar dish, solar collector, solar furnace,
sombrero,
soup bowl,
space bar,
space heater,
space shuttle,
spatula,
speedboat,
"spider web, spiders web",
spindle,
sports car, sport car,
spotlight, spot,
stage,
steam locomotive,
steel arch bridge,
steel drum,
stethoscope,
stole,
stone wall,
stopwatch, stop watch,
stove,
strainer,
streetcar, tram, tramcar, trolley, trolley car,
stretcher,
studio couch, day bed,
stupa, tope,
submarine, pigboat, sub, U-boat,
suit, suit of clothes,
sundial,
sunglass,
sunglasses, dark glasses, shades,
sunscreen, sunblock, sun blocker,
suspension bridge,
swab, swob, mop,
sweatshirt,
swimming trunks, bathing trunks,
swing,
switch, electric switch, electrical switch,
syringe,
table lamp,
tank, army tank, armored combat vehicle, armoured combat vehicle,
tape player,
teapot,
teddy, teddy bear,
television, television system,
tennis ball,
thatch, thatched roof,
theater curtain, theatre curtain,
thimble,
thresher, thrasher, threshing machine,
throne,
tile roof,
toaster,
tobacco shop, tobacconist shop, tobacconist,
toilet seat,
torch,
totem pole,
tow truck, tow car, wrecker,
toyshop,
tractor,
trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi,
tray,
trench coat,
tricycle, trike, velocipede,
trimaran,
tripod,
triumphal arch,
trolleybus, trolley coach, trackless trolley,
trombone,
tub, vat,
turnstile,
typewriter keyboard,
umbrella,
unicycle, monocycle,
upright, upright piano,
vacuum, vacuum cleaner,
vase,
vault,
velvet,
vending machine,
vestment,
viaduct,
violin, fiddle,
volleyball,
waffle iron,
wall clock,
wallet, billfold, notecase, pocketbook,
wardrobe, closet, press,
warplane, military plane,
washbasin, handbasin, washbowl, lavabo, wash-hand basin,
washer, automatic washer, washing machine,
water bottle,
water jug,
water tower,
whiskey jug,
whistle,
wig,
window screen,
window shade,
Windsor tie,
wine bottle,
wing,
wok,
wooden spoon,
wool, woolen, woollen,
worm fence, snake fence, snake-rail fence, Virginia fence,
wreck,
yawl,
yurt,
web site, website, internet site, site,
comic book,
crossword puzzle, crossword,
street sign,
traffic light, traffic signal, stoplight,
book jacket, dust cover, dust jacket, dust wrapper,
menu,
plate,
guacamole,
consomme,
hot pot, hotpot,
trifle,
ice cream, icecream,
ice lolly, lolly, lollipop, popsicle,
French loaf,
bagel, beigel,
pretzel,
cheeseburger,
hotdog, hot dog, red hot,
mashed potato,
head cabbage,
broccoli,
cauliflower,
zucchini, courgette,
spaghetti squash,
acorn squash,
butternut squash,
cucumber, cuke,
artichoke, globe artichoke,
bell pepper,
cardoon,
mushroom,
Granny Smith,
strawberry,
orange,
lemon,
fig,
pineapple, ananas,
banana,
jackfruit, jak, jack,
custard apple,
pomegranate,
hay,
carbonara,
chocolate sauce, chocolate syrup,
dough,
meat loaf, meatloaf,
pizza, pizza pie,
potpie,
burrito,
red wine,
espresso,
cup,
eggnog,
alp,
bubble,
cliff, drop, drop-off,
coral reef,
geyser,
lakeside, lakeshore,
promontory, headland, head, foreland,
sandbar, sand bar,
seashore, coast, seacoast, sea-coast,
valley, vale,
volcano,
ballplayer, baseball player,
groom, bridegroom,
scuba diver,
rapeseed,
daisy,
"yellow ladys slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",
corn,
acorn,
hip, rose hip, rosehip,
buckeye, horse chestnut, conker,
coral fungus,
agaric,
gyromitra,
stinkhorn, carrion fungus,
earthstar,
hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa,
bolete,
ear, spike, capitulum,
toilet tissue, toilet paper, bathroom tissue
python/examples/pipeline/PaddleClas/ResNet50_vd/pipeline_http_client.py 0 → 100644
浏览文件 @ e9ecf97c
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')
if __name__ == "__main__":
url = "http://127.0.0.1:18080/imagenet/prediction"
with open(os.path.join(".", "daisy.jpg"), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
data = {"key": ["image"], "value": [image]}
for i in range(100):
r = requests.post(url=url, data=json.dumps(data))
print(r.json())
python/examples/pipeline/PaddleClas/ResNet50_vd/pipeline_rpc_client.py 0 → 100644
浏览文件 @ e9ecf97c
# 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.pipeline import PipelineClient
except ImportError:
from paddle_serving_server.pipeline import PipelineClient
import numpy as np
import requests
import json
import cv2
import base64
import os
client = PipelineClient()
client.connect(['127.0.0.1:9993'])
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/PaddleClas/ResNet50_vd/resnet50_web_service.py 0 → 100644
浏览文件 @ e9ecf97c
# 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()
batch_size = len(input_dict.keys())
imgs = []
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)
img = self.seq(im)
imgs.append(img[np.newaxis, :].copy())
input_imgs = np.concatenate(imgs, axis=0)
return {"image": input_imgs}, False, None, ""
def postprocess(self, input_dicts, fetch_dict, log_id):
score_list = fetch_dict["prediction"]
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()
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# 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
```
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# Imagenet Pipeline WebService
这里以 Imagenet 服务为例来介绍 Pipeline WebService 的使用。
## 获取模型
```
sh get_model.sh
```
## 启动服务
```
python resnet50_web_service.py &>log.txt &
```
## 测试
```
python pipeline_rpc_client.py
```
python/examples/pipeline/PaddleClas/ResNet50_vd_FPGM/benchmark.py 0 → 100644
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import sys
import os
import base64
import yaml
import requests
import time
import json
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 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, gpu_id):
fin = open("config.yml", "r")
config = yaml.load(fin)
fin.close()
config["dag"]["tracer"] = {"interval_s": 10}
if device == "gpu":
config["op"]["imagenet"]["local_service_conf"]["device_type"] = 1
config["op"]["imagenet"]["local_service_conf"]["devices"] = gpu_id
else:
config["op"]["imagenet"]["local_service_conf"]["device_type"] = 0
with open("config2.yml", "w") as fout:
yaml.dump(config, fout, default_flow_style=False)
def cv2_to_base64(image):
return base64.b64encode(image).decode('utf8')
def run_http(idx, batch_size):
print("start thread ({})".format(idx))
url = "http://127.0.0.1:18080/imagenet/prediction"
start = time.time()
with open(os.path.join(".", "daisy.jpg"), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
keys, values = [], []
for i in range(batch_size):
keys.append("image_{}".format(i))
values.append(image)
data = {"key": keys, "value": values}
latency_list = []
start_time = time.time()
total_num = 0
while True:
l_start = time.time()
r = requests.post(url=url, data=json.dumps(data))
print(r.json())
l_end = time.time()
latency_list.append(l_end * 1000 - l_start * 1000)
total_num += 1
if time.time() - start_time > 20:
break
end = time.time()
return [[end - start], latency_list, [total_num]]
def multithread_http(thread, batch_size):
multi_thread_runner = MultiThreadRunner()
start = time.time()
result = multi_thread_runner.run(run_http, thread, batch_size)
end = time.time()
total_cost = end - start
avg_cost = 0
total_number = 0
for i in range(thread):
avg_cost += result[0][i]
total_number += result[2][i]
avg_cost = avg_cost / thread
print("Total cost: {}s".format(total_cost))
print("Each thread cost: {}s. ".format(avg_cost))
print("Total count: {}. ".format(total_number))
print("AVG QPS: {} samples/s".format(batch_size * total_number /
total_cost))
show_latency(result[1])
def run_rpc(thread, batch_size):
client = PipelineClient()
client.connect(['127.0.0.1:18080'])
start = time.time()
test_img_dir = "imgs/"
for img_file in os.listdir(test_img_dir):
with open(os.path.join(test_img_dir, img_file), 'rb') as file:
image_data = file.read()
image = cv2_to_base64(image_data)
start_time = time.time()
while True:
ret = client.predict(feed_dict={"image": image}, fetch=["res"])
if time.time() - start_time > 10:
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]
if device == "gpu":
gpu_id = sys.argv[5]
else:
gpu_id = None
gen_yml(device, gpu_id)
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)
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export FLAGS_profile_pipeline=1
alias python3="python3.6"
modelname="clas-ResNet50_vd_FPGM"
# HTTP
#ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3
# Create yaml，If you already have the config.yaml, ignore it.
#python3 benchmark.py yaml local_predictor 1 gpu
rm -rf profile_log_$modelname
echo "Starting HTTP Clients..."
# Start a client in each thread, tesing the case of multiple threads.
for thread_num in 1 2 4 8 12 16
do
for batch_size in 1
do
echo "----${modelname} thread num: ${thread_num} batch size: ${batch_size} mode:http ----" >>profile_log_$modelname
# Start one web service, If you start the service yourself, you can ignore it here.
#python3 web_service.py >web.log 2>&1 &
#sleep 3
# --id is the serial number of the GPU card, Must be the same as the gpu id used by the server.
nvidia-smi --id=3 --query-gpu=memory.used --format=csv -lms 1000 > gpu_use.log 2>&1 &
nvidia-smi --id=3 --query-gpu=utilization.gpu --format=csv -lms 1000 > gpu_utilization.log 2>&1 &
echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
# Start http client
python3 benchmark.py run http $thread_num $batch_size > profile 2>&1
# Collect CPU metrics, Filter data that is zero momentarily, Record the maximum value of GPU memory and the average value of GPU utilization
python3 cpu_utilization.py >> profile_log_$modelname
grep -av '^0 %' gpu_utilization.log > gpu_utilization.log.tmp
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 -F' ' '{sum+=$1} END {print "GPU_UTILIZATION:", sum/NR, sum, NR }' gpu_utilization.log.tmp >> profile_log_$modelname
# Show profiles
python3 ../../../util/show_profile.py profile $thread_num >> profile_log_$modelname
tail -n 8 profile >> profile_log_$modelname
echo '' >> profile_log_$modelname
done
done
# Kill all nvidia-smi background task.
pkill nvidia-smi
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cuda_version: "10.1"
cudnn_version: "7.6"
trt_version: "6.0"
python_version: "3.7"
gcc_version: "8.2"
paddle_version: "2.0.1"
cpu: "Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz X12"
gpu: "T4"
xpu: "None"
api: ""
owner: "cuicheng01"
model_name: "ResNet50_vd_FPGM"
model_type: "static"
model_source: "PaddleClas"
model_url: "https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_FPGM_pretrained.tar"
batch_size: 1
num_of_samples: 1000
input_shape: "3,224,224"
runtime_device: "gpu"
ir_optim: true
enable_memory_optim: true
enable_tensorrt: false
precision: "fp32"
enable_mkldnn: false
cpu_math_library_num_threads: ""
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cuda_version: "10.1"
cudnn_version: "7.6"
trt_version: "6.0"
python_version: "3.7"
gcc_version: "8.2"
paddle_version: "2.0.1"
cpu: "Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz X12"
gpu: "T4"
xpu: "None"
api: ""
owner: "cuicheng01"
model_name: "imagenet"
model_type: "static"
model_source: "PaddleClas"
model_url: "model_url_path"
batch_size: 1
num_of_samples: 1000
input_shape: "3,224,224"
runtime_device: "cpu"
ir_optim: true
enable_memory_optim: true
enable_tensorrt: false
precision: "fp32"
enable_mkldnn: false
cpu_math_library_num_threads: ""
python/examples/pipeline/PaddleClas/ResNet50_vd_FPGM/benchmark_gpu.sh 0 → 100644
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export FLAGS_profile_pipeline=1
alias python3="python3.7"
modelname="imagenet"
use_gpu=1
gpu_id="0"
benchmark_config_filename="benchmark_config.yaml"
# HTTP
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3
if [ $use_gpu -eq 1 ]; then
python3 benchmark.py yaml local_predictor 1 gpu $gpu_id
else
python3 benchmark.py yaml local_predictor 1 cpu
fi
rm -rf profile_log_$modelname
for thread_num in 1
do
for batch_size in 1
do
echo "#----imagenet thread num: $thread_num batch size: $batch_size mode:http use_gpu:$use_gpu----" >>profile_log_$modelname
rm -rf PipelineServingLogs
rm -rf cpu_utilization.py
python3 resnet50_web_service.py >web.log 2>&1 &
sleep 3
nvidia-smi --id=${gpu_id} --query-compute-apps=used_memory --format=csv -lms 100 > gpu_use.log 2>&1 &
nvidia-smi --id=${gpu_id} --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
python3 -m paddle_serving_server_gpu.profiler >>profile_log_$modelname
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
ps -ef | grep nvidia-smi | 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=$modelname}} END {print "GPU_MEM:", max}' gpu_use.log >> profile_log_$modelname
awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "GPU_UTIL:", max}' gpu_utilization.log >> profile_log_$modelname
cat benchmark.log >> profile_log_$modelname
python3 -m paddle_serving_server_gpu.parse_profile --benchmark_cfg $benchmark_config_filename --benchmark_log profile_log_$modelname
#rm -rf gpu_use.log gpu_utilization.log
done
done
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#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: 18080
rpc_port: 9993
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: 1
#uci模型路径
model_config: ResNet50_vd_FPGM/ppcls_model/
#计算硬件类型: 空缺时由devices决定(CPU/GPU)，0=cpu, 1=gpu, 2=tensorRT, 3=arm cpu, 4=kunlun xpu
device_type: 1
#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: ["save_infer_model/scale_0.tmp_1"]
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import psutil
cpu_utilization=psutil.cpu_percent(1,False)
print('CPU_UTILIZATION:', cpu_utilization)
python/examples/pipeline/PaddleClas/ResNet50_vd_FPGM/get_model.sh 0 → 100644
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wget --no-check-certificate https://paddle-serving.bj.bcebos.com/model/ResNet50_vd_FPGM.tar
tar -xf ResNet50_vd_FPGM.tar
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/PaddleClas/ResNet50_vd_FPGM/imagenet.label 0 → 100644
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tench, Tinca tinca,
goldfish, Carassius auratus,
great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias,
tiger shark, Galeocerdo cuvieri,
hammerhead, hammerhead shark,
electric ray, crampfish, numbfish, torpedo,
stingray,
cock,
hen,
ostrich, Struthio camelus,
brambling, Fringilla montifringilla,
goldfinch, Carduelis carduelis,
house finch, linnet, Carpodacus mexicanus,
junco, snowbird,
indigo bunting, indigo finch, indigo bird, Passerina cyanea,
robin, American robin, Turdus migratorius,
bulbul,
jay,
magpie,
chickadee,
water ouzel, dipper,
kite,
bald eagle, American eagle, Haliaeetus leucocephalus,
vulture,
great grey owl, great gray owl, Strix nebulosa,
European fire salamander, Salamandra salamandra,
common newt, Triturus vulgaris,
eft,
spotted salamander, Ambystoma maculatum,
axolotl, mud puppy, Ambystoma mexicanum,
bullfrog, Rana catesbeiana,
tree frog, tree-frog,
tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui,
loggerhead, loggerhead turtle, Caretta caretta,
leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea,
mud turtle,
terrapin,
box turtle, box tortoise,
banded gecko,
common iguana, iguana, Iguana iguana,
American chameleon, anole, Anolis carolinensis,
whiptail, whiptail lizard,
agama,
frilled lizard, Chlamydosaurus kingi,
alligator lizard,
Gila monster, Heloderma suspectum,
green lizard, Lacerta viridis,
African chameleon, Chamaeleo chamaeleon,
Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis,
African crocodile, Nile crocodile, Crocodylus niloticus,
American alligator, Alligator mississipiensis,
triceratops,
thunder snake, worm snake, Carphophis amoenus,
ringneck snake, ring-necked snake, ring snake,
hognose snake, puff adder, sand viper,
green snake, grass snake,
king snake, kingsnake,
garter snake, grass snake,
water snake,
vine snake,
night snake, Hypsiglena torquata,
boa constrictor, Constrictor constrictor,
rock python, rock snake, Python sebae,
Indian cobra, Naja naja,
green mamba,
sea snake,
horned viper, cerastes, sand viper, horned asp, Cerastes cornutus,
diamondback, diamondback rattlesnake, Crotalus adamanteus,
sidewinder, horned rattlesnake, Crotalus cerastes,
trilobite,
harvestman, daddy longlegs, Phalangium opilio,
scorpion,
black and gold garden spider, Argiope aurantia,
barn spider, Araneus cavaticus,
garden spider, Aranea diademata,
black widow, Latrodectus mactans,
tarantula,
wolf spider, hunting spider,
tick,
centipede,
black grouse,
ptarmigan,
ruffed grouse, partridge, Bonasa umbellus,
prairie chicken, prairie grouse, prairie fowl,
peacock,
quail,
partridge,
African grey, African gray, Psittacus erithacus,
macaw,
sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita,
lorikeet,
coucal,
bee eater,
hornbill,
hummingbird,
jacamar,
toucan,
drake,
red-breasted merganser, Mergus serrator,
goose,
black swan, Cygnus atratus,
tusker,
echidna, spiny anteater, anteater,
platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus,
wallaby, brush kangaroo,
koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus,
wombat,
jellyfish,
sea anemone, anemone,
brain coral,
flatworm, platyhelminth,
nematode, nematode worm, roundworm,
conch,
snail,
slug,
sea slug, nudibranch,
chiton, coat-of-mail shell, sea cradle, polyplacophore,
chambered nautilus, pearly nautilus, nautilus,
Dungeness crab, Cancer magister,
rock crab, Cancer irroratus,
fiddler crab,
king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica,
American lobster, Northern lobster, Maine lobster, Homarus americanus,
spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish,
crayfish, crawfish, crawdad, crawdaddy,
hermit crab,
isopod,
white stork, Ciconia ciconia,
black stork, Ciconia nigra,
spoonbill,
flamingo,
little blue heron, Egretta caerulea,
American egret, great white heron, Egretta albus,
bittern,
crane,
limpkin, Aramus pictus,
European gallinule, Porphyrio porphyrio,
American coot, marsh hen, mud hen, water hen, Fulica americana,
bustard,
ruddy turnstone, Arenaria interpres,
red-backed sandpiper, dunlin, Erolia alpina,
redshank, Tringa totanus,
dowitcher,
oystercatcher, oyster catcher,
pelican,
king penguin, Aptenodytes patagonica,
albatross, mollymawk,
grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus,
killer whale, killer, orca, grampus, sea wolf, Orcinus orca,
dugong, Dugong dugon,
sea lion,
Chihuahua,
Japanese spaniel,
Maltese dog, Maltese terrier, Maltese,
Pekinese, Pekingese, Peke,
Shih-Tzu,
Blenheim spaniel,
papillon,
toy terrier,
Rhodesian ridgeback,
Afghan hound, Afghan,
basset, basset hound,
beagle,
bloodhound, sleuthhound,
bluetick,
black-and-tan coonhound,
Walker hound, Walker foxhound,
English foxhound,
redbone,
borzoi, Russian wolfhound,
Irish wolfhound,
Italian greyhound,
whippet,
Ibizan hound, Ibizan Podenco,
Norwegian elkhound, elkhound,
otterhound, otter hound,
Saluki, gazelle hound,
Scottish deerhound, deerhound,
Weimaraner,
Staffordshire bullterrier, Staffordshire bull terrier,
American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier,
Bedlington terrier,
Border terrier,
Kerry blue terrier,
Irish terrier,
Norfolk terrier,
Norwich terrier,
Yorkshire terrier,
wire-haired fox terrier,
Lakeland terrier,
Sealyham terrier, Sealyham,
Airedale, Airedale terrier,
cairn, cairn terrier,
Australian terrier,
Dandie Dinmont, Dandie Dinmont terrier,
Boston bull, Boston terrier,
miniature schnauzer,
giant schnauzer,
standard schnauzer,
Scotch terrier, Scottish terrier, Scottie,
Tibetan terrier, chrysanthemum dog,
silky terrier, Sydney silky,
soft-coated wheaten terrier,
West Highland white terrier,
Lhasa, Lhasa apso,
flat-coated retriever,
curly-coated retriever,
golden retriever,
Labrador retriever,
Chesapeake Bay retriever,
German short-haired pointer,
vizsla, Hungarian pointer,
English setter,
Irish setter, red setter,
Gordon setter,
Brittany spaniel,
clumber, clumber spaniel,
English springer, English springer spaniel,
Welsh springer spaniel,
cocker spaniel, English cocker spaniel, cocker,
Sussex spaniel,
Irish water spaniel,
kuvasz,
schipperke,
groenendael,
malinois,
briard,
kelpie,
komondor,
Old English sheepdog, bobtail,
Shetland sheepdog, Shetland sheep dog, Shetland,
collie,
Border collie,
Bouvier des Flandres, Bouviers des Flandres,
Rottweiler,
German shepherd, German shepherd dog, German police dog, alsatian,
Doberman, Doberman pinscher,
miniature pinscher,
Greater Swiss Mountain dog,
Bernese mountain dog,
Appenzeller,
EntleBucher,
boxer,
bull mastiff,
Tibetan mastiff,
French bulldog,
Great Dane,
Saint Bernard, St Bernard,
Eskimo dog, husky,
malamute, malemute, Alaskan malamute,
Siberian husky,
dalmatian, coach dog, carriage dog,
affenpinscher, monkey pinscher, monkey dog,
basenji,
pug, pug-dog,
Leonberg,
Newfoundland, Newfoundland dog,
Great Pyrenees,
Samoyed, Samoyede,
Pomeranian,
chow, chow chow,
keeshond,
Brabancon griffon,
Pembroke, Pembroke Welsh corgi,
Cardigan, Cardigan Welsh corgi,
toy poodle,
miniature poodle,
standard poodle,
Mexican hairless,
timber wolf, grey wolf, gray wolf, Canis lupus,
white wolf, Arctic wolf, Canis lupus tundrarum,
red wolf, maned wolf, Canis rufus, Canis niger,
coyote, prairie wolf, brush wolf, Canis latrans,
dingo, warrigal, warragal, Canis dingo,
dhole, Cuon alpinus,
African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus,
hyena, hyaena,
red fox, Vulpes vulpes,
kit fox, Vulpes macrotis,
Arctic fox, white fox, Alopex lagopus,
grey fox, gray fox, Urocyon cinereoargenteus,
tabby, tabby cat,
tiger cat,
Persian cat,
Siamese cat, Siamese,
Egyptian cat,
cougar, puma, catamount, mountain lion, painter, panther, Felis concolor,
lynx, catamount,
leopard, Panthera pardus,
snow leopard, ounce, Panthera uncia,
jaguar, panther, Panthera onca, Felis onca,
lion, king of beasts, Panthera leo,
tiger, Panthera tigris,
cheetah, chetah, Acinonyx jubatus,
brown bear, bruin, Ursus arctos,
American black bear, black bear, Ursus americanus, Euarctos americanus,
ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus,
sloth bear, Melursus ursinus, Ursus ursinus,
mongoose,
meerkat, mierkat,
tiger beetle,
ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle,
ground beetle, carabid beetle,
long-horned beetle, longicorn, longicorn beetle,
leaf beetle, chrysomelid,
dung beetle,
rhinoceros beetle,
weevil,
fly,
bee,
ant, emmet, pismire,
grasshopper, hopper,
cricket,
walking stick, walkingstick, stick insect,
cockroach, roach,
mantis, mantid,
cicada, cicala,
leafhopper,
lacewing, lacewing fly,
"dragonfly, darning needle, devils darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
damselfly,
admiral,
ringlet, ringlet butterfly,
monarch, monarch butterfly, milkweed butterfly, Danaus plexippus,
cabbage butterfly,
sulphur butterfly, sulfur butterfly,
lycaenid, lycaenid butterfly,
starfish, sea star,
sea urchin,
sea cucumber, holothurian,
wood rabbit, cottontail, cottontail rabbit,
hare,
Angora, Angora rabbit,
hamster,
porcupine, hedgehog,
fox squirrel, eastern fox squirrel, Sciurus niger,
marmot,
beaver,
guinea pig, Cavia cobaya,
sorrel,
zebra,
hog, pig, grunter, squealer, Sus scrofa,
wild boar, boar, Sus scrofa,
warthog,
hippopotamus, hippo, river horse, Hippopotamus amphibius,
ox,
water buffalo, water ox, Asiatic buffalo, Bubalus bubalis,
bison,
ram, tup,
bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis,
ibex, Capra ibex,
hartebeest,
impala, Aepyceros melampus,
gazelle,
Arabian camel, dromedary, Camelus dromedarius,
llama,
weasel,
mink,
polecat, fitch, foulmart, foumart, Mustela putorius,
black-footed ferret, ferret, Mustela nigripes,
otter,
skunk, polecat, wood pussy,
badger,
armadillo,
three-toed sloth, ai, Bradypus tridactylus,
orangutan, orang, orangutang, Pongo pygmaeus,
gorilla, Gorilla gorilla,
chimpanzee, chimp, Pan troglodytes,
gibbon, Hylobates lar,
siamang, Hylobates syndactylus, Symphalangus syndactylus,
guenon, guenon monkey,
patas, hussar monkey, Erythrocebus patas,
baboon,
macaque,
langur,
colobus, colobus monkey,
proboscis monkey, Nasalis larvatus,
marmoset,
capuchin, ringtail, Cebus capucinus,
howler monkey, howler,
titi, titi monkey,
spider monkey, Ateles geoffroyi,
squirrel monkey, Saimiri sciureus,
Madagascar cat, ring-tailed lemur, Lemur catta,
indri, indris, Indri indri, Indri brevicaudatus,
Indian elephant, Elephas maximus,
African elephant, Loxodonta africana,
lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens,
giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca,
barracouta, snoek,
eel,
coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch,
rock beauty, Holocanthus tricolor,
anemone fish,
sturgeon,
gar, garfish, garpike, billfish, Lepisosteus osseus,
lionfish,
puffer, pufferfish, blowfish, globefish,
abacus,
abaya,
"academic gown, academic robe, judges robe",
accordion, piano accordion, squeeze box,
acoustic guitar,
aircraft carrier, carrier, flattop, attack aircraft carrier,
airliner,
airship, dirigible,
altar,
ambulance,
amphibian, amphibious vehicle,
analog clock,
apiary, bee house,
apron,
ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin,
assault rifle, assault gun,
backpack, back pack, knapsack, packsack, rucksack, haversack,
bakery, bakeshop, bakehouse,
balance beam, beam,
balloon,
ballpoint, ballpoint pen, ballpen, Biro,
Band Aid,
banjo,
bannister, banister, balustrade, balusters, handrail,
barbell,
barber chair,
barbershop,
barn,
barometer,
barrel, cask,
barrow, garden cart, lawn cart, wheelbarrow,
baseball,
basketball,
bassinet,
bassoon,
bathing cap, swimming cap,
bath towel,
bathtub, bathing tub, bath, tub,
beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon,
beacon, lighthouse, beacon light, pharos,
beaker,
bearskin, busby, shako,
beer bottle,
beer glass,
bell cote, bell cot,
bib,
bicycle-built-for-two, tandem bicycle, tandem,
bikini, two-piece,
binder, ring-binder,
binoculars, field glasses, opera glasses,
birdhouse,
boathouse,
bobsled, bobsleigh, bob,
bolo tie, bolo, bola tie, bola,
bonnet, poke bonnet,
bookcase,
bookshop, bookstore, bookstall,
bottlecap,
bow,
bow tie, bow-tie, bowtie,
brass, memorial tablet, plaque,
brassiere, bra, bandeau,
breakwater, groin, groyne, mole, bulwark, seawall, jetty,
breastplate, aegis, egis,
broom,
bucket, pail,
buckle,
bulletproof vest,
bullet train, bullet,
butcher shop, meat market,
cab, hack, taxi, taxicab,
caldron, cauldron,
candle, taper, wax light,
cannon,
canoe,
can opener, tin opener,
cardigan,
car mirror,
carousel, carrousel, merry-go-round, roundabout, whirligig,
"carpenters kit, tool kit",
carton,
car wheel,
cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM,
cassette,
cassette player,
castle,
catamaran,
CD player,
cello, violoncello,
cellular telephone, cellular phone, cellphone, cell, mobile phone,
chain,
chainlink fence,
chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour,
chain saw, chainsaw,
chest,
chiffonier, commode,
chime, bell, gong,
china cabinet, china closet,
Christmas stocking,
church, church building,
cinema, movie theater, movie theatre, movie house, picture palace,
cleaver, meat cleaver, chopper,
cliff dwelling,
cloak,
clog, geta, patten, sabot,
cocktail shaker,
coffee mug,
coffeepot,
coil, spiral, volute, whorl, helix,
combination lock,
computer keyboard, keypad,
confectionery, confectionary, candy store,
container ship, containership, container vessel,
convertible,
corkscrew, bottle screw,
cornet, horn, trumpet, trump,
cowboy boot,
cowboy hat, ten-gallon hat,
cradle,
crane,
crash helmet,
crate,
crib, cot,
Crock Pot,
croquet ball,
crutch,
cuirass,
dam, dike, dyke,
desk,
desktop computer,
dial telephone, dial phone,
diaper, nappy, napkin,
digital clock,
digital watch,
dining table, board,
dishrag, dishcloth,
dishwasher, dish washer, dishwashing machine,
disk brake, disc brake,
dock, dockage, docking facility,
dogsled, dog sled, dog sleigh,
dome,
doormat, welcome mat,
drilling platform, offshore rig,
drum, membranophone, tympan,
drumstick,
dumbbell,
Dutch oven,
electric fan, blower,
electric guitar,
electric locomotive,
entertainment center,
envelope,
espresso maker,
face powder,
feather boa, boa,
file, file cabinet, filing cabinet,
fireboat,
fire engine, fire truck,
fire screen, fireguard,
flagpole, flagstaff,
flute, transverse flute,
folding chair,
football helmet,
forklift,
fountain,
fountain pen,
four-poster,
freight car,
French horn, horn,
frying pan, frypan, skillet,
fur coat,
garbage truck, dustcart,
gasmask, respirator, gas helmet,
gas pump, gasoline pump, petrol pump, island dispenser,
goblet,
go-kart,
golf ball,
golfcart, golf cart,
gondola,
gong, tam-tam,
gown,
grand piano, grand,
greenhouse, nursery, glasshouse,
grille, radiator grille,
grocery store, grocery, food market, market,
guillotine,
hair slide,
hair spray,
half track,
hammer,
hamper,
hand blower, blow dryer, blow drier, hair dryer, hair drier,
hand-held computer, hand-held microcomputer,
handkerchief, hankie, hanky, hankey,
hard disc, hard disk, fixed disk,
harmonica, mouth organ, harp, mouth harp,
harp,
harvester, reaper,
hatchet,
holster,
home theater, home theatre,
honeycomb,
hook, claw,
hoopskirt, crinoline,
horizontal bar, high bar,
horse cart, horse-cart,
hourglass,
iPod,
iron, smoothing iron,
"jack-o-lantern",
jean, blue jean, denim,
jeep, landrover,
jersey, T-shirt, tee shirt,
jigsaw puzzle,
jinrikisha, ricksha, rickshaw,
joystick,
kimono,
knee pad,
knot,
lab coat, laboratory coat,
ladle,
lampshade, lamp shade,
laptop, laptop computer,
lawn mower, mower,
lens cap, lens cover,
letter opener, paper knife, paperknife,
library,
lifeboat,
lighter, light, igniter, ignitor,
limousine, limo,
liner, ocean liner,
lipstick, lip rouge,
Loafer,
lotion,
loudspeaker, speaker, speaker unit, loudspeaker system, speaker system,
"loupe, jewelers loupe",
lumbermill, sawmill,
magnetic compass,
mailbag, postbag,
mailbox, letter box,
maillot,
maillot, tank suit,
manhole cover,
maraca,
marimba, xylophone,
mask,
matchstick,
maypole,
maze, labyrinth,
measuring cup,
medicine chest, medicine cabinet,
megalith, megalithic structure,
microphone, mike,
microwave, microwave oven,
military uniform,
milk can,
minibus,
miniskirt, mini,
minivan,
missile,
mitten,
mixing bowl,
mobile home, manufactured home,
Model T,
modem,
monastery,
monitor,
moped,
mortar,
mortarboard,
mosque,
mosquito net,
motor scooter, scooter,
mountain bike, all-terrain bike, off-roader,
mountain tent,
mouse, computer mouse,
mousetrap,
moving van,
muzzle,
nail,
neck brace,
necklace,
nipple,
notebook, notebook computer,
obelisk,
oboe, hautboy, hautbois,
ocarina, sweet potato,
odometer, hodometer, mileometer, milometer,
oil filter,
organ, pipe organ,
oscilloscope, scope, cathode-ray oscilloscope, CRO,
overskirt,
oxcart,
oxygen mask,
packet,
paddle, boat paddle,
paddlewheel, paddle wheel,
padlock,
paintbrush,
"pajama, pyjama, pjs, jammies",
palace,
panpipe, pandean pipe, syrinx,
paper towel,
parachute, chute,
parallel bars, bars,
park bench,
parking meter,
passenger car, coach, carriage,
patio, terrace,
pay-phone, pay-station,
pedestal, plinth, footstall,
pencil box, pencil case,
pencil sharpener,
perfume, essence,
Petri dish,
photocopier,
pick, plectrum, plectron,
pickelhaube,
picket fence, paling,
pickup, pickup truck,
pier,
piggy bank, penny bank,
pill bottle,
pillow,
ping-pong ball,
pinwheel,
pirate, pirate ship,
pitcher, ewer,
"plane, carpenters plane, woodworking plane",
planetarium,
plastic bag,
plate rack,
plow, plough,
"plunger, plumbers helper",
Polaroid camera, Polaroid Land camera,
pole,
police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria,
poncho,
pool table, billiard table, snooker table,
pop bottle, soda bottle,
pot, flowerpot,
"potters wheel",
power drill,
prayer rug, prayer mat,
printer,
prison, prison house,
projectile, missile,
projector,
puck, hockey puck,
punching bag, punch bag, punching ball, punchball,
purse,
quill, quill pen,
quilt, comforter, comfort, puff,
racer, race car, racing car,
racket, racquet,
radiator,
radio, wireless,
radio telescope, radio reflector,
rain barrel,
recreational vehicle, RV, R.V.,
reel,
reflex camera,
refrigerator, icebox,
remote control, remote,
restaurant, eating house, eating place, eatery,
revolver, six-gun, six-shooter,
rifle,
rocking chair, rocker,
rotisserie,
rubber eraser, rubber, pencil eraser,
rugby ball,
rule, ruler,
running shoe,
safe,
safety pin,
saltshaker, salt shaker,
sandal,
sarong,
sax, saxophone,
scabbard,
scale, weighing machine,
school bus,
schooner,
scoreboard,
screen, CRT screen,
screw,
screwdriver,
seat belt, seatbelt,
sewing machine,
shield, buckler,
shoe shop, shoe-shop, shoe store,
shoji,
shopping basket,
shopping cart,
shovel,
shower cap,
shower curtain,
ski,
ski mask,
sleeping bag,
slide rule, slipstick,
sliding door,
slot, one-armed bandit,
snorkel,
snowmobile,
snowplow, snowplough,
soap dispenser,
soccer ball,
sock,
solar dish, solar collector, solar furnace,
sombrero,
soup bowl,
space bar,
space heater,
space shuttle,
spatula,
speedboat,
"spider web, spiders web",
spindle,
sports car, sport car,
spotlight, spot,
stage,
steam locomotive,
steel arch bridge,
steel drum,
stethoscope,
stole,
stone wall,
stopwatch, stop watch,
stove,
strainer,
streetcar, tram, tramcar, trolley, trolley car,
stretcher,
studio couch, day bed,
stupa, tope,
submarine, pigboat, sub, U-boat,
suit, suit of clothes,
sundial,
sunglass,
sunglasses, dark glasses, shades,
sunscreen, sunblock, sun blocker,
suspension bridge,
swab, swob, mop,
sweatshirt,
swimming trunks, bathing trunks,
swing,
switch, electric switch, electrical switch,
syringe,
table lamp,
tank, army tank, armored combat vehicle, armoured combat vehicle,
tape player,
teapot,
teddy, teddy bear,
television, television system,
tennis ball,
thatch, thatched roof,
theater curtain, theatre curtain,
thimble,
thresher, thrasher, threshing machine,
throne,
tile roof,
toaster,
tobacco shop, tobacconist shop, tobacconist,
toilet seat,
torch,
totem pole,
tow truck, tow car, wrecker,
toyshop,
tractor,
trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi,
tray,
trench coat,
tricycle, trike, velocipede,
trimaran,
tripod,
triumphal arch,
trolleybus, trolley coach, trackless trolley,
trombone,
tub, vat,
turnstile,
typewriter keyboard,
umbrella,
unicycle, monocycle,
upright, upright piano,
vacuum, vacuum cleaner,
vase,
vault,
velvet,
vending machine,
vestment,
viaduct,
violin, fiddle,
volleyball,
waffle iron,
wall clock,
wallet, billfold, notecase, pocketbook,
wardrobe, closet, press,
warplane, military plane,
washbasin, handbasin, washbowl, lavabo, wash-hand basin,
washer, automatic washer, washing machine,
water bottle,
water jug,
water tower,
whiskey jug,
whistle,
wig,
window screen,
window shade,
Windsor tie,
wine bottle,
wing,
wok,
wooden spoon,
wool, woolen, woollen,
worm fence, snake fence, snake-rail fence, Virginia fence,
wreck,
yawl,
yurt,
web site, website, internet site, site,
comic book,
crossword puzzle, crossword,
street sign,
traffic light, traffic signal, stoplight,
book jacket, dust cover, dust jacket, dust wrapper,
menu,
plate,
guacamole,
consomme,
hot pot, hotpot,
trifle,
ice cream, icecream,
ice lolly, lolly, lollipop, popsicle,
French loaf,
bagel, beigel,
pretzel,
cheeseburger,
hotdog, hot dog, red hot,
mashed potato,
head cabbage,
broccoli,
cauliflower,
zucchini, courgette,
spaghetti squash,
acorn squash,
butternut squash,
cucumber, cuke,
artichoke, globe artichoke,
bell pepper,
cardoon,
mushroom,
Granny Smith,
strawberry,
orange,
lemon,
fig,
pineapple, ananas,
banana,
jackfruit, jak, jack,
custard apple,
pomegranate,
hay,
carbonara,
chocolate sauce, chocolate syrup,
dough,
meat loaf, meatloaf,
pizza, pizza pie,
potpie,
burrito,
red wine,
espresso,
cup,
eggnog,
alp,
bubble,
cliff, drop, drop-off,
coral reef,
geyser,
lakeside, lakeshore,
promontory, headland, head, foreland,
sandbar, sand bar,
seashore, coast, seacoast, sea-coast,
valley, vale,
volcano,
ballplayer, baseball player,
groom, bridegroom,
scuba diver,
rapeseed,
daisy,
"yellow ladys slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",
corn,
acorn,
hip, rose hip, rosehip,
buckeye, horse chestnut, conker,
coral fungus,
agaric,
gyromitra,
stinkhorn, carrion fungus,
earthstar,
hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa,
bolete,
ear, spike, capitulum,
toilet tissue, toilet paper, bathroom tissue
python/examples/pipeline/PaddleClas/ResNet50_vd_FPGM/pipeline_http_client.py 0 → 100644
浏览文件 @ e9ecf97c
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')
if __name__ == "__main__":
url = "http://127.0.0.1:18080/imagenet/prediction"
with open(os.path.join(".", "daisy.jpg"), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
data = {"key": ["image"], "value": [image]}
for i in range(100):
r = requests.post(url=url, data=json.dumps(data))
print(r.json())
python/examples/pipeline/PaddleClas/ResNet50_vd_FPGM/pipeline_rpc_client.py 0 → 100644
浏览文件 @ e9ecf97c
# 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.pipeline import PipelineClient
except ImportError:
from paddle_serving_server.pipeline import PipelineClient
import numpy as np
import requests
import json
import cv2
import base64
import os
client = PipelineClient()
client.connect(['127.0.0.1:9993'])
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/PaddleClas/ResNet50_vd_FPGM/resnet50_web_service.py 0 → 100644
浏览文件 @ e9ecf97c
# 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()
batch_size = len(input_dict.keys())
imgs = []
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)
img = self.seq(im)
imgs.append(img[np.newaxis, :].copy())
input_imgs = np.concatenate(imgs, axis=0)
return {"image": input_imgs}, False, None, ""
def postprocess(self, input_dicts, fetch_dict, log_id):
score_list = fetch_dict["save_infer_model/scale_0.tmp_1"]
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/pipeline/PaddleClas/ResNet50_vd_KL/README.md 0 → 100644
浏览文件 @ e9ecf97c
# 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/PaddleClas/ResNet50_vd_KL/README_CN.md 0 → 100644
浏览文件 @ e9ecf97c
# Imagenet Pipeline WebService
这里以 Imagenet 服务为例来介绍 Pipeline WebService 的使用。
## 获取模型
```
sh get_model.sh
```
## 启动服务
```
python resnet50_web_service.py &>log.txt &
```
## 测试
```
python pipeline_rpc_client.py
```
python/examples/pipeline/PaddleClas/ResNet50_vd_KL/benchmark.py 0 → 100644
浏览文件 @ e9ecf97c
import sys
import os
import base64
import yaml
import requests
import time
import json
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 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, gpu_id):
fin = open("config.yml", "r")
config = yaml.load(fin)
fin.close()
config["dag"]["tracer"] = {"interval_s": 10}
if device == "gpu":
config["op"]["imagenet"]["local_service_conf"]["device_type"] = 1
config["op"]["imagenet"]["local_service_conf"]["devices"] = gpu_id
else:
config["op"]["imagenet"]["local_service_conf"]["device_type"] = 0
with open("config2.yml", "w") as fout:
yaml.dump(config, fout, default_flow_style=False)
def cv2_to_base64(image):
return base64.b64encode(image).decode('utf8')
def run_http(idx, batch_size):
print("start thread ({})".format(idx))
url = "http://127.0.0.1:18080/imagenet/prediction"
start = time.time()
with open(os.path.join(".", "daisy.jpg"), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
keys, values = [], []
for i in range(batch_size):
keys.append("image_{}".format(i))
values.append(image)
data = {"key": keys, "value": values}
latency_list = []
start_time = time.time()
total_num = 0
while True:
l_start = time.time()
r = requests.post(url=url, data=json.dumps(data))
print(r.json())
l_end = time.time()
latency_list.append(l_end * 1000 - l_start * 1000)
total_num += 1
if time.time() - start_time > 20:
break
end = time.time()
return [[end - start], latency_list, [total_num]]
def multithread_http(thread, batch_size):
multi_thread_runner = MultiThreadRunner()
start = time.time()
result = multi_thread_runner.run(run_http, thread, batch_size)
end = time.time()
total_cost = end - start
avg_cost = 0
total_number = 0
for i in range(thread):
avg_cost += result[0][i]
total_number += result[2][i]
avg_cost = avg_cost / thread
print("Total cost: {}s".format(total_cost))
print("Each thread cost: {}s. ".format(avg_cost))
print("Total count: {}. ".format(total_number))
print("AVG QPS: {} samples/s".format(batch_size * total_number /
total_cost))
show_latency(result[1])
def run_rpc(thread, batch_size):
client = PipelineClient()
client.connect(['127.0.0.1:18080'])
start = time.time()
test_img_dir = "imgs/"
for img_file in os.listdir(test_img_dir):
with open(os.path.join(test_img_dir, img_file), 'rb') as file:
image_data = file.read()
image = cv2_to_base64(image_data)
start_time = time.time()
while True:
ret = client.predict(feed_dict={"image": image}, fetch=["res"])
if time.time() - start_time > 10:
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]
if device == "gpu":
gpu_id = sys.argv[5]
else:
gpu_id = None
gen_yml(device, gpu_id)
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/PaddleClas/ResNet50_vd_KL/benchmark.sh 0 → 100644
浏览文件 @ e9ecf97c
export FLAGS_profile_pipeline=1
alias python3="python3.6"
modelname="clas-ResNet50_vd_KL"
# HTTP
#ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3
# Create yaml，If you already have the config.yaml, ignore it.
#python3 benchmark.py yaml local_predictor 1 gpu
rm -rf profile_log_$modelname
echo "Starting HTTP Clients..."
# Start a client in each thread, tesing the case of multiple threads.
for thread_num in 1 2 4 8 12 16
do
for batch_size in 1
do
echo "----${modelname} thread num: ${thread_num} batch size: ${batch_size} mode:http ----" >>profile_log_$modelname
# Start one web service, If you start the service yourself, you can ignore it here.
#python3 web_service.py >web.log 2>&1 &
#sleep 3
# --id is the serial number of the GPU card, Must be the same as the gpu id used by the server.
nvidia-smi --id=3 --query-gpu=memory.used --format=csv -lms 1000 > gpu_use.log 2>&1 &
nvidia-smi --id=3 --query-gpu=utilization.gpu --format=csv -lms 1000 > gpu_utilization.log 2>&1 &
echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
# Start http client
python3 benchmark.py run http $thread_num $batch_size > profile 2>&1
# Collect CPU metrics, Filter data that is zero momentarily, Record the maximum value of GPU memory and the average value of GPU utilization
python3 cpu_utilization.py >> profile_log_$modelname
grep -av '^0 %' gpu_utilization.log > gpu_utilization.log.tmp
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 -F' ' '{sum+=$1} END {print "GPU_UTILIZATION:", sum/NR, sum, NR }' gpu_utilization.log.tmp >> profile_log_$modelname
# Show profiles
python3 ../../../util/show_profile.py profile $thread_num >> profile_log_$modelname
tail -n 8 profile >> profile_log_$modelname
echo '' >> profile_log_$modelname
done
done
# Kill all nvidia-smi background task.
pkill nvidia-smi
python/examples/pipeline/PaddleClas/ResNet50_vd_KL/benchmark_config.yaml 0 → 100644
浏览文件 @ e9ecf97c
cuda_version: "10.1"
cudnn_version: "7.6"
trt_version: "6.0"
python_version: "3.7"
gcc_version: "8.2"
paddle_version: "2.0.1"
cpu: "Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz X12"
gpu: "T4"
xpu: "None"
api: ""
owner: "cuicheng01"
model_name: "ResNet50_vd_KL"
model_type: "static"
model_source: "PaddleClas"
model_url: "https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_KL_pretrained.tar"
batch_size: 1
num_of_samples: 1000
input_shape: "3,224,224"
runtime_device: "gpu"
ir_optim: true
enable_memory_optim: true
enable_tensorrt: false
precision: "fp32"
enable_mkldnn: false
cpu_math_library_num_threads: ""
python/examples/pipeline/PaddleClas/ResNet50_vd_KL/benchmark_config.yaml.template 0 → 100644
浏览文件 @ e9ecf97c
cuda_version: "10.1"
cudnn_version: "7.6"
trt_version: "6.0"
python_version: "3.7"
gcc_version: "8.2"
paddle_version: "2.0.1"
cpu: "Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz X12"
gpu: "T4"
xpu: "None"
api: ""
owner: "cuicheng01"
model_name: "imagenet"
model_type: "static"
model_source: "PaddleClas"
model_url: "model_url_path"
batch_size: 1
num_of_samples: 1000
input_shape: "3,224,224"
runtime_device: "cpu"
ir_optim: true
enable_memory_optim: true
enable_tensorrt: false
precision: "fp32"
enable_mkldnn: false
cpu_math_library_num_threads: ""
python/examples/pipeline/PaddleClas/ResNet50_vd_KL/benchmark_gpu.sh 0 → 100644
浏览文件 @ e9ecf97c
export FLAGS_profile_pipeline=1
alias python3="python3.7"
modelname="imagenet"
use_gpu=1
gpu_id="0"
benchmark_config_filename="benchmark_config.yaml"
# HTTP
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3
if [ $use_gpu -eq 1 ]; then
python3 benchmark.py yaml local_predictor 1 gpu $gpu_id
else
python3 benchmark.py yaml local_predictor 1 cpu
fi
rm -rf profile_log_$modelname
for thread_num in 1
do
for batch_size in 1
do
echo "#----imagenet thread num: $thread_num batch size: $batch_size mode:http use_gpu:$use_gpu----" >>profile_log_$modelname
rm -rf PipelineServingLogs
rm -rf cpu_utilization.py
python3 resnet50_web_service.py >web.log 2>&1 &
sleep 3
nvidia-smi --id=${gpu_id} --query-compute-apps=used_memory --format=csv -lms 100 > gpu_use.log 2>&1 &
nvidia-smi --id=${gpu_id} --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
python3 -m paddle_serving_server_gpu.profiler >>profile_log_$modelname
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
ps -ef | grep nvidia-smi | 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=$modelname}} END {print "GPU_MEM:", max}' gpu_use.log >> profile_log_$modelname
awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "GPU_UTIL:", max}' gpu_utilization.log >> profile_log_$modelname
cat benchmark.log >> profile_log_$modelname
python3 -m paddle_serving_server_gpu.parse_profile --benchmark_cfg $benchmark_config_filename --benchmark_log profile_log_$modelname
#rm -rf gpu_use.log gpu_utilization.log
done
done
python/examples/pipeline/PaddleClas/ResNet50_vd_KL/config.yml 0 → 100644
浏览文件 @ e9ecf97c
#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: 18080
rpc_port: 9993
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: 1
#uci模型路径
model_config: ResNet50_vd_KL/ppcls_model/
#计算硬件类型: 空缺时由devices决定(CPU/GPU)，0=cpu, 1=gpu, 2=tensorRT, 3=arm cpu, 4=kunlun xpu
device_type: 1
#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: ["save_infer_model/scale_0.tmp_0"]
python/examples/pipeline/PaddleClas/ResNet50_vd_KL/cpu_utilization.py 0 → 100644
浏览文件 @ e9ecf97c
import psutil
cpu_utilization=psutil.cpu_percent(1,False)
print('CPU_UTILIZATION:', cpu_utilization)
python/examples/pipeline/PaddleClas/ResNet50_vd_KL/get_model.sh 0 → 100644
浏览文件 @ e9ecf97c
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/model/ResNet50_vd_KL.tar
tar -xf ResNet50_vd_KL.tar
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/PaddleClas/ResNet50_vd_KL/imagenet.label 0 → 100644
浏览文件 @ e9ecf97c
tench, Tinca tinca,
goldfish, Carassius auratus,
great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias,
tiger shark, Galeocerdo cuvieri,
hammerhead, hammerhead shark,
electric ray, crampfish, numbfish, torpedo,
stingray,
cock,
hen,
ostrich, Struthio camelus,
brambling, Fringilla montifringilla,
goldfinch, Carduelis carduelis,
house finch, linnet, Carpodacus mexicanus,
junco, snowbird,
indigo bunting, indigo finch, indigo bird, Passerina cyanea,
robin, American robin, Turdus migratorius,
bulbul,
jay,
magpie,
chickadee,
water ouzel, dipper,
kite,
bald eagle, American eagle, Haliaeetus leucocephalus,
vulture,
great grey owl, great gray owl, Strix nebulosa,
European fire salamander, Salamandra salamandra,
common newt, Triturus vulgaris,
eft,
spotted salamander, Ambystoma maculatum,
axolotl, mud puppy, Ambystoma mexicanum,
bullfrog, Rana catesbeiana,
tree frog, tree-frog,
tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui,
loggerhead, loggerhead turtle, Caretta caretta,
leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea,
mud turtle,
terrapin,
box turtle, box tortoise,
banded gecko,
common iguana, iguana, Iguana iguana,
American chameleon, anole, Anolis carolinensis,
whiptail, whiptail lizard,
agama,
frilled lizard, Chlamydosaurus kingi,
alligator lizard,
Gila monster, Heloderma suspectum,
green lizard, Lacerta viridis,
African chameleon, Chamaeleo chamaeleon,
Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis,
African crocodile, Nile crocodile, Crocodylus niloticus,
American alligator, Alligator mississipiensis,
triceratops,
thunder snake, worm snake, Carphophis amoenus,
ringneck snake, ring-necked snake, ring snake,
hognose snake, puff adder, sand viper,
green snake, grass snake,
king snake, kingsnake,
garter snake, grass snake,
water snake,
vine snake,
night snake, Hypsiglena torquata,
boa constrictor, Constrictor constrictor,
rock python, rock snake, Python sebae,
Indian cobra, Naja naja,
green mamba,
sea snake,
horned viper, cerastes, sand viper, horned asp, Cerastes cornutus,
diamondback, diamondback rattlesnake, Crotalus adamanteus,
sidewinder, horned rattlesnake, Crotalus cerastes,
trilobite,
harvestman, daddy longlegs, Phalangium opilio,
scorpion,
black and gold garden spider, Argiope aurantia,
barn spider, Araneus cavaticus,
garden spider, Aranea diademata,
black widow, Latrodectus mactans,
tarantula,
wolf spider, hunting spider,
tick,
centipede,
black grouse,
ptarmigan,
ruffed grouse, partridge, Bonasa umbellus,
prairie chicken, prairie grouse, prairie fowl,
peacock,
quail,
partridge,
African grey, African gray, Psittacus erithacus,
macaw,
sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita,
lorikeet,
coucal,
bee eater,
hornbill,
hummingbird,
jacamar,
toucan,
drake,
red-breasted merganser, Mergus serrator,
goose,
black swan, Cygnus atratus,
tusker,
echidna, spiny anteater, anteater,
platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus,
wallaby, brush kangaroo,
koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus,
wombat,
jellyfish,
sea anemone, anemone,
brain coral,
flatworm, platyhelminth,
nematode, nematode worm, roundworm,
conch,
snail,
slug,
sea slug, nudibranch,
chiton, coat-of-mail shell, sea cradle, polyplacophore,
chambered nautilus, pearly nautilus, nautilus,
Dungeness crab, Cancer magister,
rock crab, Cancer irroratus,
fiddler crab,
king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica,
American lobster, Northern lobster, Maine lobster, Homarus americanus,
spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish,
crayfish, crawfish, crawdad, crawdaddy,
hermit crab,
isopod,
white stork, Ciconia ciconia,
black stork, Ciconia nigra,
spoonbill,
flamingo,
little blue heron, Egretta caerulea,
American egret, great white heron, Egretta albus,
bittern,
crane,
limpkin, Aramus pictus,
European gallinule, Porphyrio porphyrio,
American coot, marsh hen, mud hen, water hen, Fulica americana,
bustard,
ruddy turnstone, Arenaria interpres,
red-backed sandpiper, dunlin, Erolia alpina,
redshank, Tringa totanus,
dowitcher,
oystercatcher, oyster catcher,
pelican,
king penguin, Aptenodytes patagonica,
albatross, mollymawk,
grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus,
killer whale, killer, orca, grampus, sea wolf, Orcinus orca,
dugong, Dugong dugon,
sea lion,
Chihuahua,
Japanese spaniel,
Maltese dog, Maltese terrier, Maltese,
Pekinese, Pekingese, Peke,
Shih-Tzu,
Blenheim spaniel,
papillon,
toy terrier,
Rhodesian ridgeback,
Afghan hound, Afghan,
basset, basset hound,
beagle,
bloodhound, sleuthhound,
bluetick,
black-and-tan coonhound,
Walker hound, Walker foxhound,
English foxhound,
redbone,
borzoi, Russian wolfhound,
Irish wolfhound,
Italian greyhound,
whippet,
Ibizan hound, Ibizan Podenco,
Norwegian elkhound, elkhound,
otterhound, otter hound,
Saluki, gazelle hound,
Scottish deerhound, deerhound,
Weimaraner,
Staffordshire bullterrier, Staffordshire bull terrier,
American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier,
Bedlington terrier,
Border terrier,
Kerry blue terrier,
Irish terrier,
Norfolk terrier,
Norwich terrier,
Yorkshire terrier,
wire-haired fox terrier,
Lakeland terrier,
Sealyham terrier, Sealyham,
Airedale, Airedale terrier,
cairn, cairn terrier,
Australian terrier,
Dandie Dinmont, Dandie Dinmont terrier,
Boston bull, Boston terrier,
miniature schnauzer,
giant schnauzer,
standard schnauzer,
Scotch terrier, Scottish terrier, Scottie,
Tibetan terrier, chrysanthemum dog,
silky terrier, Sydney silky,
soft-coated wheaten terrier,
West Highland white terrier,
Lhasa, Lhasa apso,
flat-coated retriever,
curly-coated retriever,
golden retriever,
Labrador retriever,
Chesapeake Bay retriever,
German short-haired pointer,
vizsla, Hungarian pointer,
English setter,
Irish setter, red setter,
Gordon setter,
Brittany spaniel,
clumber, clumber spaniel,
English springer, English springer spaniel,
Welsh springer spaniel,
cocker spaniel, English cocker spaniel, cocker,
Sussex spaniel,
Irish water spaniel,
kuvasz,
schipperke,
groenendael,
malinois,
briard,
kelpie,
komondor,
Old English sheepdog, bobtail,
Shetland sheepdog, Shetland sheep dog, Shetland,
collie,
Border collie,
Bouvier des Flandres, Bouviers des Flandres,
Rottweiler,
German shepherd, German shepherd dog, German police dog, alsatian,
Doberman, Doberman pinscher,
miniature pinscher,
Greater Swiss Mountain dog,
Bernese mountain dog,
Appenzeller,
EntleBucher,
boxer,
bull mastiff,
Tibetan mastiff,
French bulldog,
Great Dane,
Saint Bernard, St Bernard,
Eskimo dog, husky,
malamute, malemute, Alaskan malamute,
Siberian husky,
dalmatian, coach dog, carriage dog,
affenpinscher, monkey pinscher, monkey dog,
basenji,
pug, pug-dog,
Leonberg,
Newfoundland, Newfoundland dog,
Great Pyrenees,
Samoyed, Samoyede,
Pomeranian,
chow, chow chow,
keeshond,
Brabancon griffon,
Pembroke, Pembroke Welsh corgi,
Cardigan, Cardigan Welsh corgi,
toy poodle,
miniature poodle,
standard poodle,
Mexican hairless,
timber wolf, grey wolf, gray wolf, Canis lupus,
white wolf, Arctic wolf, Canis lupus tundrarum,
red wolf, maned wolf, Canis rufus, Canis niger,
coyote, prairie wolf, brush wolf, Canis latrans,
dingo, warrigal, warragal, Canis dingo,
dhole, Cuon alpinus,
African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus,
hyena, hyaena,
red fox, Vulpes vulpes,
kit fox, Vulpes macrotis,
Arctic fox, white fox, Alopex lagopus,
grey fox, gray fox, Urocyon cinereoargenteus,
tabby, tabby cat,
tiger cat,
Persian cat,
Siamese cat, Siamese,
Egyptian cat,
cougar, puma, catamount, mountain lion, painter, panther, Felis concolor,
lynx, catamount,
leopard, Panthera pardus,
snow leopard, ounce, Panthera uncia,
jaguar, panther, Panthera onca, Felis onca,
lion, king of beasts, Panthera leo,
tiger, Panthera tigris,
cheetah, chetah, Acinonyx jubatus,
brown bear, bruin, Ursus arctos,
American black bear, black bear, Ursus americanus, Euarctos americanus,
ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus,
sloth bear, Melursus ursinus, Ursus ursinus,
mongoose,
meerkat, mierkat,
tiger beetle,
ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle,
ground beetle, carabid beetle,
long-horned beetle, longicorn, longicorn beetle,
leaf beetle, chrysomelid,
dung beetle,
rhinoceros beetle,
weevil,
fly,
bee,
ant, emmet, pismire,
grasshopper, hopper,
cricket,
walking stick, walkingstick, stick insect,
cockroach, roach,
mantis, mantid,
cicada, cicala,
leafhopper,
lacewing, lacewing fly,
"dragonfly, darning needle, devils darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
damselfly,
admiral,
ringlet, ringlet butterfly,
monarch, monarch butterfly, milkweed butterfly, Danaus plexippus,
cabbage butterfly,
sulphur butterfly, sulfur butterfly,
lycaenid, lycaenid butterfly,
starfish, sea star,
sea urchin,
sea cucumber, holothurian,
wood rabbit, cottontail, cottontail rabbit,
hare,
Angora, Angora rabbit,
hamster,
porcupine, hedgehog,
fox squirrel, eastern fox squirrel, Sciurus niger,
marmot,
beaver,
guinea pig, Cavia cobaya,
sorrel,
zebra,
hog, pig, grunter, squealer, Sus scrofa,
wild boar, boar, Sus scrofa,
warthog,
hippopotamus, hippo, river horse, Hippopotamus amphibius,
ox,
water buffalo, water ox, Asiatic buffalo, Bubalus bubalis,
bison,
ram, tup,
bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis,
ibex, Capra ibex,
hartebeest,
impala, Aepyceros melampus,
gazelle,
Arabian camel, dromedary, Camelus dromedarius,
llama,
weasel,
mink,
polecat, fitch, foulmart, foumart, Mustela putorius,
black-footed ferret, ferret, Mustela nigripes,
otter,
skunk, polecat, wood pussy,
badger,
armadillo,
three-toed sloth, ai, Bradypus tridactylus,
orangutan, orang, orangutang, Pongo pygmaeus,
gorilla, Gorilla gorilla,
chimpanzee, chimp, Pan troglodytes,
gibbon, Hylobates lar,
siamang, Hylobates syndactylus, Symphalangus syndactylus,
guenon, guenon monkey,
patas, hussar monkey, Erythrocebus patas,
baboon,
macaque,
langur,
colobus, colobus monkey,
proboscis monkey, Nasalis larvatus,
marmoset,
capuchin, ringtail, Cebus capucinus,
howler monkey, howler,
titi, titi monkey,
spider monkey, Ateles geoffroyi,
squirrel monkey, Saimiri sciureus,
Madagascar cat, ring-tailed lemur, Lemur catta,
indri, indris, Indri indri, Indri brevicaudatus,
Indian elephant, Elephas maximus,
African elephant, Loxodonta africana,
lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens,
giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca,
barracouta, snoek,
eel,
coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch,
rock beauty, Holocanthus tricolor,
anemone fish,
sturgeon,
gar, garfish, garpike, billfish, Lepisosteus osseus,
lionfish,
puffer, pufferfish, blowfish, globefish,
abacus,
abaya,
"academic gown, academic robe, judges robe",
accordion, piano accordion, squeeze box,
acoustic guitar,
aircraft carrier, carrier, flattop, attack aircraft carrier,
airliner,
airship, dirigible,
altar,
ambulance,
amphibian, amphibious vehicle,
analog clock,
apiary, bee house,
apron,
ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin,
assault rifle, assault gun,
backpack, back pack, knapsack, packsack, rucksack, haversack,
bakery, bakeshop, bakehouse,
balance beam, beam,
balloon,
ballpoint, ballpoint pen, ballpen, Biro,
Band Aid,
banjo,
bannister, banister, balustrade, balusters, handrail,
barbell,
barber chair,
barbershop,
barn,
barometer,
barrel, cask,
barrow, garden cart, lawn cart, wheelbarrow,
baseball,
basketball,
bassinet,
bassoon,
bathing cap, swimming cap,
bath towel,
bathtub, bathing tub, bath, tub,
beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon,
beacon, lighthouse, beacon light, pharos,
beaker,
bearskin, busby, shako,
beer bottle,
beer glass,
bell cote, bell cot,
bib,
bicycle-built-for-two, tandem bicycle, tandem,
bikini, two-piece,
binder, ring-binder,
binoculars, field glasses, opera glasses,
birdhouse,
boathouse,
bobsled, bobsleigh, bob,
bolo tie, bolo, bola tie, bola,
bonnet, poke bonnet,
bookcase,
bookshop, bookstore, bookstall,
bottlecap,
bow,
bow tie, bow-tie, bowtie,
brass, memorial tablet, plaque,
brassiere, bra, bandeau,
breakwater, groin, groyne, mole, bulwark, seawall, jetty,
breastplate, aegis, egis,
broom,
bucket, pail,
buckle,
bulletproof vest,
bullet train, bullet,
butcher shop, meat market,
cab, hack, taxi, taxicab,
caldron, cauldron,
candle, taper, wax light,
cannon,
canoe,
can opener, tin opener,
cardigan,
car mirror,
carousel, carrousel, merry-go-round, roundabout, whirligig,
"carpenters kit, tool kit",
carton,
car wheel,
cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM,
cassette,
cassette player,
castle,
catamaran,
CD player,
cello, violoncello,
cellular telephone, cellular phone, cellphone, cell, mobile phone,
chain,
chainlink fence,
chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour,
chain saw, chainsaw,
chest,
chiffonier, commode,
chime, bell, gong,
china cabinet, china closet,
Christmas stocking,
church, church building,
cinema, movie theater, movie theatre, movie house, picture palace,
cleaver, meat cleaver, chopper,
cliff dwelling,
cloak,
clog, geta, patten, sabot,
cocktail shaker,
coffee mug,
coffeepot,
coil, spiral, volute, whorl, helix,
combination lock,
computer keyboard, keypad,
confectionery, confectionary, candy store,
container ship, containership, container vessel,
convertible,
corkscrew, bottle screw,
cornet, horn, trumpet, trump,
cowboy boot,
cowboy hat, ten-gallon hat,
cradle,
crane,
crash helmet,
crate,
crib, cot,
Crock Pot,
croquet ball,
crutch,
cuirass,
dam, dike, dyke,
desk,
desktop computer,
dial telephone, dial phone,
diaper, nappy, napkin,
digital clock,
digital watch,
dining table, board,
dishrag, dishcloth,
dishwasher, dish washer, dishwashing machine,
disk brake, disc brake,
dock, dockage, docking facility,
dogsled, dog sled, dog sleigh,
dome,
doormat, welcome mat,
drilling platform, offshore rig,
drum, membranophone, tympan,
drumstick,
dumbbell,
Dutch oven,
electric fan, blower,
electric guitar,
electric locomotive,
entertainment center,
envelope,
espresso maker,
face powder,
feather boa, boa,
file, file cabinet, filing cabinet,
fireboat,
fire engine, fire truck,
fire screen, fireguard,
flagpole, flagstaff,
flute, transverse flute,
folding chair,
football helmet,
forklift,
fountain,
fountain pen,
four-poster,
freight car,
French horn, horn,
frying pan, frypan, skillet,
fur coat,
garbage truck, dustcart,
gasmask, respirator, gas helmet,
gas pump, gasoline pump, petrol pump, island dispenser,
goblet,
go-kart,
golf ball,
golfcart, golf cart,
gondola,
gong, tam-tam,
gown,
grand piano, grand,
greenhouse, nursery, glasshouse,
grille, radiator grille,
grocery store, grocery, food market, market,
guillotine,
hair slide,
hair spray,
half track,
hammer,
hamper,
hand blower, blow dryer, blow drier, hair dryer, hair drier,
hand-held computer, hand-held microcomputer,
handkerchief, hankie, hanky, hankey,
hard disc, hard disk, fixed disk,
harmonica, mouth organ, harp, mouth harp,
harp,
harvester, reaper,
hatchet,
holster,
home theater, home theatre,
honeycomb,
hook, claw,
hoopskirt, crinoline,
horizontal bar, high bar,
horse cart, horse-cart,
hourglass,
iPod,
iron, smoothing iron,
"jack-o-lantern",
jean, blue jean, denim,
jeep, landrover,
jersey, T-shirt, tee shirt,
jigsaw puzzle,
jinrikisha, ricksha, rickshaw,
joystick,
kimono,
knee pad,
knot,
lab coat, laboratory coat,
ladle,
lampshade, lamp shade,
laptop, laptop computer,
lawn mower, mower,
lens cap, lens cover,
letter opener, paper knife, paperknife,
library,
lifeboat,
lighter, light, igniter, ignitor,
limousine, limo,
liner, ocean liner,
lipstick, lip rouge,
Loafer,
lotion,
loudspeaker, speaker, speaker unit, loudspeaker system, speaker system,
"loupe, jewelers loupe",
lumbermill, sawmill,
magnetic compass,
mailbag, postbag,
mailbox, letter box,
maillot,
maillot, tank suit,
manhole cover,
maraca,
marimba, xylophone,
mask,
matchstick,
maypole,
maze, labyrinth,
measuring cup,
medicine chest, medicine cabinet,
megalith, megalithic structure,
microphone, mike,
microwave, microwave oven,
military uniform,
milk can,
minibus,
miniskirt, mini,
minivan,
missile,
mitten,
mixing bowl,
mobile home, manufactured home,
Model T,
modem,
monastery,
monitor,
moped,
mortar,
mortarboard,
mosque,
mosquito net,
motor scooter, scooter,
mountain bike, all-terrain bike, off-roader,
mountain tent,
mouse, computer mouse,
mousetrap,
moving van,
muzzle,
nail,
neck brace,
necklace,
nipple,
notebook, notebook computer,
obelisk,
oboe, hautboy, hautbois,
ocarina, sweet potato,
odometer, hodometer, mileometer, milometer,
oil filter,
organ, pipe organ,
oscilloscope, scope, cathode-ray oscilloscope, CRO,
overskirt,
oxcart,
oxygen mask,
packet,
paddle, boat paddle,
paddlewheel, paddle wheel,
padlock,
paintbrush,
"pajama, pyjama, pjs, jammies",
palace,
panpipe, pandean pipe, syrinx,
paper towel,
parachute, chute,
parallel bars, bars,
park bench,
parking meter,
passenger car, coach, carriage,
patio, terrace,
pay-phone, pay-station,
pedestal, plinth, footstall,
pencil box, pencil case,
pencil sharpener,
perfume, essence,
Petri dish,
photocopier,
pick, plectrum, plectron,
pickelhaube,
picket fence, paling,
pickup, pickup truck,
pier,
piggy bank, penny bank,
pill bottle,
pillow,
ping-pong ball,
pinwheel,
pirate, pirate ship,
pitcher, ewer,
"plane, carpenters plane, woodworking plane",
planetarium,
plastic bag,
plate rack,
plow, plough,
"plunger, plumbers helper",
Polaroid camera, Polaroid Land camera,
pole,
police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria,
poncho,
pool table, billiard table, snooker table,
pop bottle, soda bottle,
pot, flowerpot,
"potters wheel",
power drill,
prayer rug, prayer mat,
printer,
prison, prison house,
projectile, missile,
projector,
puck, hockey puck,
punching bag, punch bag, punching ball, punchball,
purse,
quill, quill pen,
quilt, comforter, comfort, puff,
racer, race car, racing car,
racket, racquet,
radiator,
radio, wireless,
radio telescope, radio reflector,
rain barrel,
recreational vehicle, RV, R.V.,
reel,
reflex camera,
refrigerator, icebox,
remote control, remote,
restaurant, eating house, eating place, eatery,
revolver, six-gun, six-shooter,
rifle,
rocking chair, rocker,
rotisserie,
rubber eraser, rubber, pencil eraser,
rugby ball,
rule, ruler,
running shoe,
safe,
safety pin,
saltshaker, salt shaker,
sandal,
sarong,
sax, saxophone,
scabbard,
scale, weighing machine,
school bus,
schooner,
scoreboard,
screen, CRT screen,
screw,
screwdriver,
seat belt, seatbelt,
sewing machine,
shield, buckler,
shoe shop, shoe-shop, shoe store,
shoji,
shopping basket,
shopping cart,
shovel,
shower cap,
shower curtain,
ski,
ski mask,
sleeping bag,
slide rule, slipstick,
sliding door,
slot, one-armed bandit,
snorkel,
snowmobile,
snowplow, snowplough,
soap dispenser,
soccer ball,
sock,
solar dish, solar collector, solar furnace,
sombrero,
soup bowl,
space bar,
space heater,
space shuttle,
spatula,
speedboat,
"spider web, spiders web",
spindle,
sports car, sport car,
spotlight, spot,
stage,
steam locomotive,
steel arch bridge,
steel drum,
stethoscope,
stole,
stone wall,
stopwatch, stop watch,
stove,
strainer,
streetcar, tram, tramcar, trolley, trolley car,
stretcher,
studio couch, day bed,
stupa, tope,
submarine, pigboat, sub, U-boat,
suit, suit of clothes,
sundial,
sunglass,
sunglasses, dark glasses, shades,
sunscreen, sunblock, sun blocker,
suspension bridge,
swab, swob, mop,
sweatshirt,
swimming trunks, bathing trunks,
swing,
switch, electric switch, electrical switch,
syringe,
table lamp,
tank, army tank, armored combat vehicle, armoured combat vehicle,
tape player,
teapot,
teddy, teddy bear,
television, television system,
tennis ball,
thatch, thatched roof,
theater curtain, theatre curtain,
thimble,
thresher, thrasher, threshing machine,
throne,
tile roof,
toaster,
tobacco shop, tobacconist shop, tobacconist,
toilet seat,
torch,
totem pole,
tow truck, tow car, wrecker,
toyshop,
tractor,
trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi,
tray,
trench coat,
tricycle, trike, velocipede,
trimaran,
tripod,
triumphal arch,
trolleybus, trolley coach, trackless trolley,
trombone,
tub, vat,
turnstile,
typewriter keyboard,
umbrella,
unicycle, monocycle,
upright, upright piano,
vacuum, vacuum cleaner,
vase,
vault,
velvet,
vending machine,
vestment,
viaduct,
violin, fiddle,
volleyball,
waffle iron,
wall clock,
wallet, billfold, notecase, pocketbook,
wardrobe, closet, press,
warplane, military plane,
washbasin, handbasin, washbowl, lavabo, wash-hand basin,
washer, automatic washer, washing machine,
water bottle,
water jug,
water tower,
whiskey jug,
whistle,
wig,
window screen,
window shade,
Windsor tie,
wine bottle,
wing,
wok,
wooden spoon,
wool, woolen, woollen,
worm fence, snake fence, snake-rail fence, Virginia fence,
wreck,
yawl,
yurt,
web site, website, internet site, site,
comic book,
crossword puzzle, crossword,
street sign,
traffic light, traffic signal, stoplight,
book jacket, dust cover, dust jacket, dust wrapper,
menu,
plate,
guacamole,
consomme,
hot pot, hotpot,
trifle,
ice cream, icecream,
ice lolly, lolly, lollipop, popsicle,
French loaf,
bagel, beigel,
pretzel,
cheeseburger,
hotdog, hot dog, red hot,
mashed potato,
head cabbage,
broccoli,
cauliflower,
zucchini, courgette,
spaghetti squash,
acorn squash,
butternut squash,
cucumber, cuke,
artichoke, globe artichoke,
bell pepper,
cardoon,
mushroom,
Granny Smith,
strawberry,
orange,
lemon,
fig,
pineapple, ananas,
banana,
jackfruit, jak, jack,
custard apple,
pomegranate,
hay,
carbonara,
chocolate sauce, chocolate syrup,
dough,
meat loaf, meatloaf,
pizza, pizza pie,
potpie,
burrito,
red wine,
espresso,
cup,
eggnog,
alp,
bubble,
cliff, drop, drop-off,
coral reef,
geyser,
lakeside, lakeshore,
promontory, headland, head, foreland,
sandbar, sand bar,
seashore, coast, seacoast, sea-coast,
valley, vale,
volcano,
ballplayer, baseball player,
groom, bridegroom,
scuba diver,
rapeseed,
daisy,
"yellow ladys slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",
corn,
acorn,
hip, rose hip, rosehip,
buckeye, horse chestnut, conker,
coral fungus,
agaric,
gyromitra,
stinkhorn, carrion fungus,
earthstar,
hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa,
bolete,
ear, spike, capitulum,
toilet tissue, toilet paper, bathroom tissue
python/examples/pipeline/PaddleClas/ResNet50_vd_KL/pipeline_http_client.py 0 → 100644
浏览文件 @ e9ecf97c
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')
if __name__ == "__main__":
url = "http://127.0.0.1:18080/imagenet/prediction"
with open(os.path.join(".", "daisy.jpg"), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
data = {"key": ["image"], "value": [image]}
for i in range(100):
r = requests.post(url=url, data=json.dumps(data))
print(r.json())
python/examples/pipeline/PaddleClas/ResNet50_vd_KL/pipeline_rpc_client.py 0 → 100644
浏览文件 @ e9ecf97c
# 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.pipeline import PipelineClient
except ImportError:
from paddle_serving_server.pipeline import PipelineClient
import numpy as np
import requests
import json
import cv2
import base64
import os
client = PipelineClient()
client.connect(['127.0.0.1:9993'])
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/PaddleClas/ResNet50_vd_KL/resnet50_web_service.py 0 → 100644
浏览文件 @ e9ecf97c
# 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()
batch_size = len(input_dict.keys())
imgs = []
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)
img = self.seq(im)
imgs.append(img[np.newaxis, :].copy())
input_imgs = np.concatenate(imgs, axis=0)
return {"inputs": input_imgs}, False, None, ""
def postprocess(self, input_dicts, fetch_dict, log_id):
score_list = fetch_dict["save_infer_model/scale_0.tmp_0"]
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/pipeline/PaddleClas/ResNet50_vd_PACT/README.md 0 → 100644
浏览文件 @ e9ecf97c
# 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/PaddleClas/ResNet50_vd_PACT/README_CN.md 0 → 100644
浏览文件 @ e9ecf97c
# Imagenet Pipeline WebService
这里以 Imagenet 服务为例来介绍 Pipeline WebService 的使用。
## 获取模型
```
sh get_model.sh
```
## 启动服务
```
python resnet50_web_service.py &>log.txt &
```
## 测试
```
python pipeline_rpc_client.py
```
python/examples/pipeline/PaddleClas/ResNet50_vd_PACT/benchmark.py 0 → 100644
浏览文件 @ e9ecf97c
import sys
import os
import base64
import yaml
import requests
import time
import json
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 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, gpu_id):
fin = open("config.yml", "r")
config = yaml.load(fin)
fin.close()
config["dag"]["tracer"] = {"interval_s": 10}
if device == "gpu":
config["op"]["imagenet"]["local_service_conf"]["device_type"] = 1
config["op"]["imagenet"]["local_service_conf"]["devices"] = gpu_id
else:
config["op"]["imagenet"]["local_service_conf"]["device_type"] = 0
with open("config2.yml", "w") as fout:
yaml.dump(config, fout, default_flow_style=False)
def cv2_to_base64(image):
return base64.b64encode(image).decode('utf8')
def run_http(idx, batch_size):
print("start thread ({})".format(idx))
url = "http://127.0.0.1:18080/imagenet/prediction"
start = time.time()
with open(os.path.join(".", "daisy.jpg"), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
keys, values = [], []
for i in range(batch_size):
keys.append("image_{}".format(i))
values.append(image)
data = {"key": keys, "value": values}
latency_list = []
start_time = time.time()
total_num = 0
while True:
l_start = time.time()
r = requests.post(url=url, data=json.dumps(data))
print(r.json())
l_end = time.time()
latency_list.append(l_end * 1000 - l_start * 1000)
total_num += 1
if time.time() - start_time > 20:
break
end = time.time()
return [[end - start], latency_list, [total_num]]
def multithread_http(thread, batch_size):
multi_thread_runner = MultiThreadRunner()
start = time.time()
result = multi_thread_runner.run(run_http, thread, batch_size)
end = time.time()
total_cost = end - start
avg_cost = 0
total_number = 0
for i in range(thread):
avg_cost += result[0][i]
total_number += result[2][i]
avg_cost = avg_cost / thread
print("Total cost: {}s".format(total_cost))
print("Each thread cost: {}s. ".format(avg_cost))
print("Total count: {}. ".format(total_number))
print("AVG QPS: {} samples/s".format(batch_size * total_number /
total_cost))
show_latency(result[1])
def run_rpc(thread, batch_size):
client = PipelineClient()
client.connect(['127.0.0.1:18080'])
start = time.time()
test_img_dir = "imgs/"
for img_file in os.listdir(test_img_dir):
with open(os.path.join(test_img_dir, img_file), 'rb') as file:
image_data = file.read()
image = cv2_to_base64(image_data)
start_time = time.time()
while True:
ret = client.predict(feed_dict={"image": image}, fetch=["res"])
if time.time() - start_time > 10:
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]
if device == "gpu":
gpu_id = sys.argv[5]
else:
gpu_id = None
gen_yml(device, gpu_id)
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/PaddleClas/ResNet50_vd_PACT/benchmark.sh 0 → 100644
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export FLAGS_profile_pipeline=1
alias python3="python3.6"
modelname="clas-ResNet50_vd_PACT"
# HTTP
#ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3
# Create yaml，If you already have the config.yaml, ignore it.
#python3 benchmark.py yaml local_predictor 1 gpu
rm -rf profile_log_$modelname
echo "Starting HTTP Clients..."
# Start a client in each thread, tesing the case of multiple threads.
for thread_num in 1 2 4 8 12 16
do
for batch_size in 1
do
echo "----${modelname} thread num: ${thread_num} batch size: ${batch_size} mode:http ----" >>profile_log_$modelname
# Start one web service, If you start the service yourself, you can ignore it here.
#python3 web_service.py >web.log 2>&1 &
#sleep 3
# --id is the serial number of the GPU card, Must be the same as the gpu id used by the server.
nvidia-smi --id=3 --query-gpu=memory.used --format=csv -lms 1000 > gpu_use.log 2>&1 &
nvidia-smi --id=3 --query-gpu=utilization.gpu --format=csv -lms 1000 > gpu_utilization.log 2>&1 &
echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
# Start http client
python3 benchmark.py run http $thread_num $batch_size > profile 2>&1
# Collect CPU metrics, Filter data that is zero momentarily, Record the maximum value of GPU memory and the average value of GPU utilization
python3 cpu_utilization.py >> profile_log_$modelname
grep -av '^0 %' gpu_utilization.log > gpu_utilization.log.tmp
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 -F' ' '{sum+=$1} END {print "GPU_UTILIZATION:", sum/NR, sum, NR }' gpu_utilization.log.tmp >> profile_log_$modelname
# Show profiles
python3 ../../../util/show_profile.py profile $thread_num >> profile_log_$modelname
tail -n 8 profile >> profile_log_$modelname
echo '' >> profile_log_$modelname
done
done
# Kill all nvidia-smi background task.
pkill nvidia-smi
python/examples/pipeline/PaddleClas/ResNet50_vd_PACT/benchmark_config.yaml 0 → 100644
浏览文件 @ e9ecf97c
cuda_version: "10.1"
cudnn_version: "7.6"
trt_version: "6.0"
python_version: "3.7"
gcc_version: "8.2"
paddle_version: "2.0.1"
cpu: "Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz X12"
gpu: "T4"
xpu: "None"
api: ""
owner: "cuicheng01"
model_name: "ResNet50_vd_PACT"
model_type: "static"
model_source: "PaddleClas"
model_url: "https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_PACT_pretrained.tar"
batch_size: 1
num_of_samples: 1000
input_shape: "3,224,224"
runtime_device: "gpu"
ir_optim: true
enable_memory_optim: true
enable_tensorrt: false
precision: "fp32"
enable_mkldnn: false
cpu_math_library_num_threads: ""
python/examples/pipeline/PaddleClas/ResNet50_vd_PACT/benchmark_config.yaml.template 0 → 100644
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cuda_version: "10.1"
cudnn_version: "7.6"
trt_version: "6.0"
python_version: "3.7"
gcc_version: "8.2"
paddle_version: "2.0.1"
cpu: "Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz X12"
gpu: "T4"
xpu: "None"
api: ""
owner: "cuicheng01"
model_name: "imagenet"
model_type: "static"
model_source: "PaddleClas"
model_url: "model_url_path"
batch_size: 1
num_of_samples: 1000
input_shape: "3,224,224"
runtime_device: "cpu"
ir_optim: true
enable_memory_optim: true
enable_tensorrt: false
precision: "fp32"
enable_mkldnn: false
cpu_math_library_num_threads: ""
python/examples/pipeline/PaddleClas/ResNet50_vd_PACT/benchmark_gpu.sh 0 → 100644
浏览文件 @ e9ecf97c
export FLAGS_profile_pipeline=1
alias python3="python3.7"
modelname="imagenet"
use_gpu=1
gpu_id="0"
benchmark_config_filename="benchmark_config.yaml"
# HTTP
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3
if [ $use_gpu -eq 1 ]; then
python3 benchmark.py yaml local_predictor 1 gpu $gpu_id
else
python3 benchmark.py yaml local_predictor 1 cpu
fi
rm -rf profile_log_$modelname
for thread_num in 1
do
for batch_size in 1
do
echo "#----imagenet thread num: $thread_num batch size: $batch_size mode:http use_gpu:$use_gpu----" >>profile_log_$modelname
rm -rf PipelineServingLogs
rm -rf cpu_utilization.py
python3 resnet50_web_service.py >web.log 2>&1 &
sleep 3
nvidia-smi --id=${gpu_id} --query-compute-apps=used_memory --format=csv -lms 100 > gpu_use.log 2>&1 &
nvidia-smi --id=${gpu_id} --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
python3 -m paddle_serving_server_gpu.profiler >>profile_log_$modelname
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
ps -ef | grep nvidia-smi | 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=$modelname}} END {print "GPU_MEM:", max}' gpu_use.log >> profile_log_$modelname
awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "GPU_UTIL:", max}' gpu_utilization.log >> profile_log_$modelname
cat benchmark.log >> profile_log_$modelname
python3 -m paddle_serving_server_gpu.parse_profile --benchmark_cfg $benchmark_config_filename --benchmark_log profile_log_$modelname
#rm -rf gpu_use.log gpu_utilization.log
done
done
python/examples/pipeline/PaddleClas/ResNet50_vd_PACT/config.yml 0 → 100644
浏览文件 @ e9ecf97c
#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: 18080
rpc_port: 9993
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: 1
#uci模型路径
model_config: ResNet50_vd_PACT/ppcls_model/
#计算硬件类型: 空缺时由devices决定(CPU/GPU)，0=cpu, 1=gpu, 2=tensorRT, 3=arm cpu, 4=kunlun xpu
device_type: 1
#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: ["save_infer_model/scale_0.tmp_1"]
python/examples/pipeline/PaddleClas/ResNet50_vd_PACT/cpu_utilization.py 0 → 100644
浏览文件 @ e9ecf97c
import psutil
cpu_utilization=psutil.cpu_percent(1,False)
print('CPU_UTILIZATION:', cpu_utilization)
python/examples/pipeline/PaddleClas/ResNet50_vd_PACT/get_model.sh 0 → 100644
浏览文件 @ e9ecf97c
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/model/ResNet50_vd_PACT.tar
tar -xf ResNet50_vd_PACT.tar
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/PaddleClas/ResNet50_vd_PACT/imagenet.label 0 → 100644
浏览文件 @ e9ecf97c
tench, Tinca tinca,
goldfish, Carassius auratus,
great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias,
tiger shark, Galeocerdo cuvieri,
hammerhead, hammerhead shark,
electric ray, crampfish, numbfish, torpedo,
stingray,
cock,
hen,
ostrich, Struthio camelus,
brambling, Fringilla montifringilla,
goldfinch, Carduelis carduelis,
house finch, linnet, Carpodacus mexicanus,
junco, snowbird,
indigo bunting, indigo finch, indigo bird, Passerina cyanea,
robin, American robin, Turdus migratorius,
bulbul,
jay,
magpie,
chickadee,
water ouzel, dipper,
kite,
bald eagle, American eagle, Haliaeetus leucocephalus,
vulture,
great grey owl, great gray owl, Strix nebulosa,
European fire salamander, Salamandra salamandra,
common newt, Triturus vulgaris,
eft,
spotted salamander, Ambystoma maculatum,
axolotl, mud puppy, Ambystoma mexicanum,
bullfrog, Rana catesbeiana,
tree frog, tree-frog,
tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui,
loggerhead, loggerhead turtle, Caretta caretta,
leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea,
mud turtle,
terrapin,
box turtle, box tortoise,
banded gecko,
common iguana, iguana, Iguana iguana,
American chameleon, anole, Anolis carolinensis,
whiptail, whiptail lizard,
agama,
frilled lizard, Chlamydosaurus kingi,
alligator lizard,
Gila monster, Heloderma suspectum,
green lizard, Lacerta viridis,
African chameleon, Chamaeleo chamaeleon,
Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis,
African crocodile, Nile crocodile, Crocodylus niloticus,
American alligator, Alligator mississipiensis,
triceratops,
thunder snake, worm snake, Carphophis amoenus,
ringneck snake, ring-necked snake, ring snake,
hognose snake, puff adder, sand viper,
green snake, grass snake,
king snake, kingsnake,
garter snake, grass snake,
water snake,
vine snake,
night snake, Hypsiglena torquata,
boa constrictor, Constrictor constrictor,
rock python, rock snake, Python sebae,
Indian cobra, Naja naja,
green mamba,
sea snake,
horned viper, cerastes, sand viper, horned asp, Cerastes cornutus,
diamondback, diamondback rattlesnake, Crotalus adamanteus,
sidewinder, horned rattlesnake, Crotalus cerastes,
trilobite,
harvestman, daddy longlegs, Phalangium opilio,
scorpion,
black and gold garden spider, Argiope aurantia,
barn spider, Araneus cavaticus,
garden spider, Aranea diademata,
black widow, Latrodectus mactans,
tarantula,
wolf spider, hunting spider,
tick,
centipede,
black grouse,
ptarmigan,
ruffed grouse, partridge, Bonasa umbellus,
prairie chicken, prairie grouse, prairie fowl,
peacock,
quail,
partridge,
African grey, African gray, Psittacus erithacus,
macaw,
sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita,
lorikeet,
coucal,
bee eater,
hornbill,
hummingbird,
jacamar,
toucan,
drake,
red-breasted merganser, Mergus serrator,
goose,
black swan, Cygnus atratus,
tusker,
echidna, spiny anteater, anteater,
platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus,
wallaby, brush kangaroo,
koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus,
wombat,
jellyfish,
sea anemone, anemone,
brain coral,
flatworm, platyhelminth,
nematode, nematode worm, roundworm,
conch,
snail,
slug,
sea slug, nudibranch,
chiton, coat-of-mail shell, sea cradle, polyplacophore,
chambered nautilus, pearly nautilus, nautilus,
Dungeness crab, Cancer magister,
rock crab, Cancer irroratus,
fiddler crab,
king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica,
American lobster, Northern lobster, Maine lobster, Homarus americanus,
spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish,
crayfish, crawfish, crawdad, crawdaddy,
hermit crab,
isopod,
white stork, Ciconia ciconia,
black stork, Ciconia nigra,
spoonbill,
flamingo,
little blue heron, Egretta caerulea,
American egret, great white heron, Egretta albus,
bittern,
crane,
limpkin, Aramus pictus,
European gallinule, Porphyrio porphyrio,
American coot, marsh hen, mud hen, water hen, Fulica americana,
bustard,
ruddy turnstone, Arenaria interpres,
red-backed sandpiper, dunlin, Erolia alpina,
redshank, Tringa totanus,
dowitcher,
oystercatcher, oyster catcher,
pelican,
king penguin, Aptenodytes patagonica,
albatross, mollymawk,
grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus,
killer whale, killer, orca, grampus, sea wolf, Orcinus orca,
dugong, Dugong dugon,
sea lion,
Chihuahua,
Japanese spaniel,
Maltese dog, Maltese terrier, Maltese,
Pekinese, Pekingese, Peke,
Shih-Tzu,
Blenheim spaniel,
papillon,
toy terrier,
Rhodesian ridgeback,
Afghan hound, Afghan,
basset, basset hound,
beagle,
bloodhound, sleuthhound,
bluetick,
black-and-tan coonhound,
Walker hound, Walker foxhound,
English foxhound,
redbone,
borzoi, Russian wolfhound,
Irish wolfhound,
Italian greyhound,
whippet,
Ibizan hound, Ibizan Podenco,
Norwegian elkhound, elkhound,
otterhound, otter hound,
Saluki, gazelle hound,
Scottish deerhound, deerhound,
Weimaraner,
Staffordshire bullterrier, Staffordshire bull terrier,
American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier,
Bedlington terrier,
Border terrier,
Kerry blue terrier,
Irish terrier,
Norfolk terrier,
Norwich terrier,
Yorkshire terrier,
wire-haired fox terrier,
Lakeland terrier,
Sealyham terrier, Sealyham,
Airedale, Airedale terrier,
cairn, cairn terrier,
Australian terrier,
Dandie Dinmont, Dandie Dinmont terrier,
Boston bull, Boston terrier,
miniature schnauzer,
giant schnauzer,
standard schnauzer,
Scotch terrier, Scottish terrier, Scottie,
Tibetan terrier, chrysanthemum dog,
silky terrier, Sydney silky,
soft-coated wheaten terrier,
West Highland white terrier,
Lhasa, Lhasa apso,
flat-coated retriever,
curly-coated retriever,
golden retriever,
Labrador retriever,
Chesapeake Bay retriever,
German short-haired pointer,
vizsla, Hungarian pointer,
English setter,
Irish setter, red setter,
Gordon setter,
Brittany spaniel,
clumber, clumber spaniel,
English springer, English springer spaniel,
Welsh springer spaniel,
cocker spaniel, English cocker spaniel, cocker,
Sussex spaniel,
Irish water spaniel,
kuvasz,
schipperke,
groenendael,
malinois,
briard,
kelpie,
komondor,
Old English sheepdog, bobtail,
Shetland sheepdog, Shetland sheep dog, Shetland,
collie,
Border collie,
Bouvier des Flandres, Bouviers des Flandres,
Rottweiler,
German shepherd, German shepherd dog, German police dog, alsatian,
Doberman, Doberman pinscher,
miniature pinscher,
Greater Swiss Mountain dog,
Bernese mountain dog,
Appenzeller,
EntleBucher,
boxer,
bull mastiff,
Tibetan mastiff,
French bulldog,
Great Dane,
Saint Bernard, St Bernard,
Eskimo dog, husky,
malamute, malemute, Alaskan malamute,
Siberian husky,
dalmatian, coach dog, carriage dog,
affenpinscher, monkey pinscher, monkey dog,
basenji,
pug, pug-dog,
Leonberg,
Newfoundland, Newfoundland dog,
Great Pyrenees,
Samoyed, Samoyede,
Pomeranian,
chow, chow chow,
keeshond,
Brabancon griffon,
Pembroke, Pembroke Welsh corgi,
Cardigan, Cardigan Welsh corgi,
toy poodle,
miniature poodle,
standard poodle,
Mexican hairless,
timber wolf, grey wolf, gray wolf, Canis lupus,
white wolf, Arctic wolf, Canis lupus tundrarum,
red wolf, maned wolf, Canis rufus, Canis niger,
coyote, prairie wolf, brush wolf, Canis latrans,
dingo, warrigal, warragal, Canis dingo,
dhole, Cuon alpinus,
African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus,
hyena, hyaena,
red fox, Vulpes vulpes,
kit fox, Vulpes macrotis,
Arctic fox, white fox, Alopex lagopus,
grey fox, gray fox, Urocyon cinereoargenteus,
tabby, tabby cat,
tiger cat,
Persian cat,
Siamese cat, Siamese,
Egyptian cat,
cougar, puma, catamount, mountain lion, painter, panther, Felis concolor,
lynx, catamount,
leopard, Panthera pardus,
snow leopard, ounce, Panthera uncia,
jaguar, panther, Panthera onca, Felis onca,
lion, king of beasts, Panthera leo,
tiger, Panthera tigris,
cheetah, chetah, Acinonyx jubatus,
brown bear, bruin, Ursus arctos,
American black bear, black bear, Ursus americanus, Euarctos americanus,
ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus,
sloth bear, Melursus ursinus, Ursus ursinus,
mongoose,
meerkat, mierkat,
tiger beetle,
ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle,
ground beetle, carabid beetle,
long-horned beetle, longicorn, longicorn beetle,
leaf beetle, chrysomelid,
dung beetle,
rhinoceros beetle,
weevil,
fly,
bee,
ant, emmet, pismire,
grasshopper, hopper,
cricket,
walking stick, walkingstick, stick insect,
cockroach, roach,
mantis, mantid,
cicada, cicala,
leafhopper,
lacewing, lacewing fly,
"dragonfly, darning needle, devils darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
damselfly,
admiral,
ringlet, ringlet butterfly,
monarch, monarch butterfly, milkweed butterfly, Danaus plexippus,
cabbage butterfly,
sulphur butterfly, sulfur butterfly,
lycaenid, lycaenid butterfly,
starfish, sea star,
sea urchin,
sea cucumber, holothurian,
wood rabbit, cottontail, cottontail rabbit,
hare,
Angora, Angora rabbit,
hamster,
porcupine, hedgehog,
fox squirrel, eastern fox squirrel, Sciurus niger,
marmot,
beaver,
guinea pig, Cavia cobaya,
sorrel,
zebra,
hog, pig, grunter, squealer, Sus scrofa,
wild boar, boar, Sus scrofa,
warthog,
hippopotamus, hippo, river horse, Hippopotamus amphibius,
ox,
water buffalo, water ox, Asiatic buffalo, Bubalus bubalis,
bison,
ram, tup,
bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis,
ibex, Capra ibex,
hartebeest,
impala, Aepyceros melampus,
gazelle,
Arabian camel, dromedary, Camelus dromedarius,
llama,
weasel,
mink,
polecat, fitch, foulmart, foumart, Mustela putorius,
black-footed ferret, ferret, Mustela nigripes,
otter,
skunk, polecat, wood pussy,
badger,
armadillo,
three-toed sloth, ai, Bradypus tridactylus,
orangutan, orang, orangutang, Pongo pygmaeus,
gorilla, Gorilla gorilla,
chimpanzee, chimp, Pan troglodytes,
gibbon, Hylobates lar,
siamang, Hylobates syndactylus, Symphalangus syndactylus,
guenon, guenon monkey,
patas, hussar monkey, Erythrocebus patas,
baboon,
macaque,
langur,
colobus, colobus monkey,
proboscis monkey, Nasalis larvatus,
marmoset,
capuchin, ringtail, Cebus capucinus,
howler monkey, howler,
titi, titi monkey,
spider monkey, Ateles geoffroyi,
squirrel monkey, Saimiri sciureus,
Madagascar cat, ring-tailed lemur, Lemur catta,
indri, indris, Indri indri, Indri brevicaudatus,
Indian elephant, Elephas maximus,
African elephant, Loxodonta africana,
lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens,
giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca,
barracouta, snoek,
eel,
coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch,
rock beauty, Holocanthus tricolor,
anemone fish,
sturgeon,
gar, garfish, garpike, billfish, Lepisosteus osseus,
lionfish,
puffer, pufferfish, blowfish, globefish,
abacus,
abaya,
"academic gown, academic robe, judges robe",
accordion, piano accordion, squeeze box,
acoustic guitar,
aircraft carrier, carrier, flattop, attack aircraft carrier,
airliner,
airship, dirigible,
altar,
ambulance,
amphibian, amphibious vehicle,
analog clock,
apiary, bee house,
apron,
ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin,
assault rifle, assault gun,
backpack, back pack, knapsack, packsack, rucksack, haversack,
bakery, bakeshop, bakehouse,
balance beam, beam,
balloon,
ballpoint, ballpoint pen, ballpen, Biro,
Band Aid,
banjo,
bannister, banister, balustrade, balusters, handrail,
barbell,
barber chair,
barbershop,
barn,
barometer,
barrel, cask,
barrow, garden cart, lawn cart, wheelbarrow,
baseball,
basketball,
bassinet,
bassoon,
bathing cap, swimming cap,
bath towel,
bathtub, bathing tub, bath, tub,
beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon,
beacon, lighthouse, beacon light, pharos,
beaker,
bearskin, busby, shako,
beer bottle,
beer glass,
bell cote, bell cot,
bib,
bicycle-built-for-two, tandem bicycle, tandem,
bikini, two-piece,
binder, ring-binder,
binoculars, field glasses, opera glasses,
birdhouse,
boathouse,
bobsled, bobsleigh, bob,
bolo tie, bolo, bola tie, bola,
bonnet, poke bonnet,
bookcase,
bookshop, bookstore, bookstall,
bottlecap,
bow,
bow tie, bow-tie, bowtie,
brass, memorial tablet, plaque,
brassiere, bra, bandeau,
breakwater, groin, groyne, mole, bulwark, seawall, jetty,
breastplate, aegis, egis,
broom,
bucket, pail,
buckle,
bulletproof vest,
bullet train, bullet,
butcher shop, meat market,
cab, hack, taxi, taxicab,
caldron, cauldron,
candle, taper, wax light,
cannon,
canoe,
can opener, tin opener,
cardigan,
car mirror,
carousel, carrousel, merry-go-round, roundabout, whirligig,
"carpenters kit, tool kit",
carton,
car wheel,
cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM,
cassette,
cassette player,
castle,
catamaran,
CD player,
cello, violoncello,
cellular telephone, cellular phone, cellphone, cell, mobile phone,
chain,
chainlink fence,
chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour,
chain saw, chainsaw,
chest,
chiffonier, commode,
chime, bell, gong,
china cabinet, china closet,
Christmas stocking,
church, church building,
cinema, movie theater, movie theatre, movie house, picture palace,
cleaver, meat cleaver, chopper,
cliff dwelling,
cloak,
clog, geta, patten, sabot,
cocktail shaker,
coffee mug,
coffeepot,
coil, spiral, volute, whorl, helix,
combination lock,
computer keyboard, keypad,
confectionery, confectionary, candy store,
container ship, containership, container vessel,
convertible,
corkscrew, bottle screw,
cornet, horn, trumpet, trump,
cowboy boot,
cowboy hat, ten-gallon hat,
cradle,
crane,
crash helmet,
crate,
crib, cot,
Crock Pot,
croquet ball,
crutch,
cuirass,
dam, dike, dyke,
desk,
desktop computer,
dial telephone, dial phone,
diaper, nappy, napkin,
digital clock,
digital watch,
dining table, board,
dishrag, dishcloth,
dishwasher, dish washer, dishwashing machine,
disk brake, disc brake,
dock, dockage, docking facility,
dogsled, dog sled, dog sleigh,
dome,
doormat, welcome mat,
drilling platform, offshore rig,
drum, membranophone, tympan,
drumstick,
dumbbell,
Dutch oven,
electric fan, blower,
electric guitar,
electric locomotive,
entertainment center,
envelope,
espresso maker,
face powder,
feather boa, boa,
file, file cabinet, filing cabinet,
fireboat,
fire engine, fire truck,
fire screen, fireguard,
flagpole, flagstaff,
flute, transverse flute,
folding chair,
football helmet,
forklift,
fountain,
fountain pen,
four-poster,
freight car,
French horn, horn,
frying pan, frypan, skillet,
fur coat,
garbage truck, dustcart,
gasmask, respirator, gas helmet,
gas pump, gasoline pump, petrol pump, island dispenser,
goblet,
go-kart,
golf ball,
golfcart, golf cart,
gondola,
gong, tam-tam,
gown,
grand piano, grand,
greenhouse, nursery, glasshouse,
grille, radiator grille,
grocery store, grocery, food market, market,
guillotine,
hair slide,
hair spray,
half track,
hammer,
hamper,
hand blower, blow dryer, blow drier, hair dryer, hair drier,
hand-held computer, hand-held microcomputer,
handkerchief, hankie, hanky, hankey,
hard disc, hard disk, fixed disk,
harmonica, mouth organ, harp, mouth harp,
harp,
harvester, reaper,
hatchet,
holster,
home theater, home theatre,
honeycomb,
hook, claw,
hoopskirt, crinoline,
horizontal bar, high bar,
horse cart, horse-cart,
hourglass,
iPod,
iron, smoothing iron,
"jack-o-lantern",
jean, blue jean, denim,
jeep, landrover,
jersey, T-shirt, tee shirt,
jigsaw puzzle,
jinrikisha, ricksha, rickshaw,
joystick,
kimono,
knee pad,
knot,
lab coat, laboratory coat,
ladle,
lampshade, lamp shade,
laptop, laptop computer,
lawn mower, mower,
lens cap, lens cover,
letter opener, paper knife, paperknife,
library,
lifeboat,
lighter, light, igniter, ignitor,
limousine, limo,
liner, ocean liner,
lipstick, lip rouge,
Loafer,
lotion,
loudspeaker, speaker, speaker unit, loudspeaker system, speaker system,
"loupe, jewelers loupe",
lumbermill, sawmill,
magnetic compass,
mailbag, postbag,
mailbox, letter box,
maillot,
maillot, tank suit,
manhole cover,
maraca,
marimba, xylophone,
mask,
matchstick,
maypole,
maze, labyrinth,
measuring cup,
medicine chest, medicine cabinet,
megalith, megalithic structure,
microphone, mike,
microwave, microwave oven,
military uniform,
milk can,
minibus,
miniskirt, mini,
minivan,
missile,
mitten,
mixing bowl,
mobile home, manufactured home,
Model T,
modem,
monastery,
monitor,
moped,
mortar,
mortarboard,
mosque,
mosquito net,
motor scooter, scooter,
mountain bike, all-terrain bike, off-roader,
mountain tent,
mouse, computer mouse,
mousetrap,
moving van,
muzzle,
nail,
neck brace,
necklace,
nipple,
notebook, notebook computer,
obelisk,
oboe, hautboy, hautbois,
ocarina, sweet potato,
odometer, hodometer, mileometer, milometer,
oil filter,
organ, pipe organ,
oscilloscope, scope, cathode-ray oscilloscope, CRO,
overskirt,
oxcart,
oxygen mask,
packet,
paddle, boat paddle,
paddlewheel, paddle wheel,
padlock,
paintbrush,
"pajama, pyjama, pjs, jammies",
palace,
panpipe, pandean pipe, syrinx,
paper towel,
parachute, chute,
parallel bars, bars,
park bench,
parking meter,
passenger car, coach, carriage,
patio, terrace,
pay-phone, pay-station,
pedestal, plinth, footstall,
pencil box, pencil case,
pencil sharpener,
perfume, essence,
Petri dish,
photocopier,
pick, plectrum, plectron,
pickelhaube,
picket fence, paling,
pickup, pickup truck,
pier,
piggy bank, penny bank,
pill bottle,
pillow,
ping-pong ball,
pinwheel,
pirate, pirate ship,
pitcher, ewer,
"plane, carpenters plane, woodworking plane",
planetarium,
plastic bag,
plate rack,
plow, plough,
"plunger, plumbers helper",
Polaroid camera, Polaroid Land camera,
pole,
police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria,
poncho,
pool table, billiard table, snooker table,
pop bottle, soda bottle,
pot, flowerpot,
"potters wheel",
power drill,
prayer rug, prayer mat,
printer,
prison, prison house,
projectile, missile,
projector,
puck, hockey puck,
punching bag, punch bag, punching ball, punchball,
purse,
quill, quill pen,
quilt, comforter, comfort, puff,
racer, race car, racing car,
racket, racquet,
radiator,
radio, wireless,
radio telescope, radio reflector,
rain barrel,
recreational vehicle, RV, R.V.,
reel,
reflex camera,
refrigerator, icebox,
remote control, remote,
restaurant, eating house, eating place, eatery,
revolver, six-gun, six-shooter,
rifle,
rocking chair, rocker,
rotisserie,
rubber eraser, rubber, pencil eraser,
rugby ball,
rule, ruler,
running shoe,
safe,
safety pin,
saltshaker, salt shaker,
sandal,
sarong,
sax, saxophone,
scabbard,
scale, weighing machine,
school bus,
schooner,
scoreboard,
screen, CRT screen,
screw,
screwdriver,
seat belt, seatbelt,
sewing machine,
shield, buckler,
shoe shop, shoe-shop, shoe store,
shoji,
shopping basket,
shopping cart,
shovel,
shower cap,
shower curtain,
ski,
ski mask,
sleeping bag,
slide rule, slipstick,
sliding door,
slot, one-armed bandit,
snorkel,
snowmobile,
snowplow, snowplough,
soap dispenser,
soccer ball,
sock,
solar dish, solar collector, solar furnace,
sombrero,
soup bowl,
space bar,
space heater,
space shuttle,
spatula,
speedboat,
"spider web, spiders web",
spindle,
sports car, sport car,
spotlight, spot,
stage,
steam locomotive,
steel arch bridge,
steel drum,
stethoscope,
stole,
stone wall,
stopwatch, stop watch,
stove,
strainer,
streetcar, tram, tramcar, trolley, trolley car,
stretcher,
studio couch, day bed,
stupa, tope,
submarine, pigboat, sub, U-boat,
suit, suit of clothes,
sundial,
sunglass,
sunglasses, dark glasses, shades,
sunscreen, sunblock, sun blocker,
suspension bridge,
swab, swob, mop,
sweatshirt,
swimming trunks, bathing trunks,
swing,
switch, electric switch, electrical switch,
syringe,
table lamp,
tank, army tank, armored combat vehicle, armoured combat vehicle,
tape player,
teapot,
teddy, teddy bear,
television, television system,
tennis ball,
thatch, thatched roof,
theater curtain, theatre curtain,
thimble,
thresher, thrasher, threshing machine,
throne,
tile roof,
toaster,
tobacco shop, tobacconist shop, tobacconist,
toilet seat,
torch,
totem pole,
tow truck, tow car, wrecker,
toyshop,
tractor,
trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi,
tray,
trench coat,
tricycle, trike, velocipede,
trimaran,
tripod,
triumphal arch,
trolleybus, trolley coach, trackless trolley,
trombone,
tub, vat,
turnstile,
typewriter keyboard,
umbrella,
unicycle, monocycle,
upright, upright piano,
vacuum, vacuum cleaner,
vase,
vault,
velvet,
vending machine,
vestment,
viaduct,
violin, fiddle,
volleyball,
waffle iron,
wall clock,
wallet, billfold, notecase, pocketbook,
wardrobe, closet, press,
warplane, military plane,
washbasin, handbasin, washbowl, lavabo, wash-hand basin,
washer, automatic washer, washing machine,
water bottle,
water jug,
water tower,
whiskey jug,
whistle,
wig,
window screen,
window shade,
Windsor tie,
wine bottle,
wing,
wok,
wooden spoon,
wool, woolen, woollen,
worm fence, snake fence, snake-rail fence, Virginia fence,
wreck,
yawl,
yurt,
web site, website, internet site, site,
comic book,
crossword puzzle, crossword,
street sign,
traffic light, traffic signal, stoplight,
book jacket, dust cover, dust jacket, dust wrapper,
menu,
plate,
guacamole,
consomme,
hot pot, hotpot,
trifle,
ice cream, icecream,
ice lolly, lolly, lollipop, popsicle,
French loaf,
bagel, beigel,
pretzel,
cheeseburger,
hotdog, hot dog, red hot,
mashed potato,
head cabbage,
broccoli,
cauliflower,
zucchini, courgette,
spaghetti squash,
acorn squash,
butternut squash,
cucumber, cuke,
artichoke, globe artichoke,
bell pepper,
cardoon,
mushroom,
Granny Smith,
strawberry,
orange,
lemon,
fig,
pineapple, ananas,
banana,
jackfruit, jak, jack,
custard apple,
pomegranate,
hay,
carbonara,
chocolate sauce, chocolate syrup,
dough,
meat loaf, meatloaf,
pizza, pizza pie,
potpie,
burrito,
red wine,
espresso,
cup,
eggnog,
alp,
bubble,
cliff, drop, drop-off,
coral reef,
geyser,
lakeside, lakeshore,
promontory, headland, head, foreland,
sandbar, sand bar,
seashore, coast, seacoast, sea-coast,
valley, vale,
volcano,
ballplayer, baseball player,
groom, bridegroom,
scuba diver,
rapeseed,
daisy,
"yellow ladys slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",
corn,
acorn,
hip, rose hip, rosehip,
buckeye, horse chestnut, conker,
coral fungus,
agaric,
gyromitra,
stinkhorn, carrion fungus,
earthstar,
hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa,
bolete,
ear, spike, capitulum,
toilet tissue, toilet paper, bathroom tissue
python/examples/pipeline/PaddleClas/ResNet50_vd_PACT/pipeline_http_client.py 0 → 100644
浏览文件 @ e9ecf97c
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')
if __name__ == "__main__":
url = "http://127.0.0.1:18080/imagenet/prediction"
with open(os.path.join(".", "daisy.jpg"), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
data = {"key": ["image"], "value": [image]}
for i in range(100):
r = requests.post(url=url, data=json.dumps(data))
print(r.json())
python/examples/pipeline/PaddleClas/ResNet50_vd_PACT/pipeline_rpc_client.py 0 → 100644
浏览文件 @ e9ecf97c
# 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.pipeline import PipelineClient
except ImportError:
from paddle_serving_server.pipeline import PipelineClient
import numpy as np
import requests
import json
import cv2
import base64
import os
client = PipelineClient()
client.connect(['127.0.0.1:9993'])
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/PaddleClas/ResNet50_vd_PACT/resnet50_web_service.py 0 → 100644
浏览文件 @ e9ecf97c
# 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()
batch_size = len(input_dict.keys())
imgs = []
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)
img = self.seq(im)
imgs.append(img[np.newaxis, :].copy())
input_imgs = np.concatenate(imgs, axis=0)
return {"inputs": input_imgs}, False, None, ""
def postprocess(self, input_dicts, fetch_dict, log_id):
score_list = fetch_dict["save_infer_model/scale_0.tmp_1"]
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/pipeline/PaddleClas/ResNet_V2_50/README.md 0 → 100644
浏览文件 @ e9ecf97c
# Imagenet Pipeline WebService
This document will takes Imagenet service as an example to introduce how to use Pipeline WebService.
## Get model
```
python -m paddle_serving_app.package --get_model resnet_v2_50_imagenet
tar -xzvf resnet_v2_50_imagenet.tar.gz
```
## Start server
```
python resnet50_web_service.py &>log.txt &
```
## RPC test
```
python pipeline_rpc_client.py
```
python/examples/pipeline/PaddleClas/ResNet_V2_50/README_CN.md 0 → 100644
浏览文件 @ e9ecf97c
# Imagenet Pipeline WebService
这里以 Imagenet 服务为例来介绍 Pipeline WebService 的使用。
## 获取模型
```
python -m paddle_serving_app.package --get_model resnet_v2_50_imagenet
tar -xzvf resnet_v2_50_imagenet.tar.gz
```
## 启动服务
```
python resnet50_web_service.py &>log.txt &
```
## 测试
```
python pipeline_rpc_client.py
```
python/examples/pipeline/PaddleClas/ResNet_V2_50/benchmark.py 0 → 100644
浏览文件 @ e9ecf97c
import sys
import os
import base64
import yaml
import requests
import time
import json
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 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, gpu_id):
fin = open("config.yml", "r")
config = yaml.load(fin)
fin.close()
config["dag"]["tracer"] = {"interval_s": 10}
if device == "gpu":
config["op"]["imagenet"]["local_service_conf"]["device_type"] = 1
config["op"]["imagenet"]["local_service_conf"]["devices"] = gpu_id
else:
config["op"]["imagenet"]["local_service_conf"]["device_type"] = 0
with open("config2.yml", "w") as fout:
yaml.dump(config, fout, default_flow_style=False)
def cv2_to_base64(image):
return base64.b64encode(image).decode('utf8')
def run_http(idx, batch_size):
print("start thread ({})".format(idx))
url = "http://127.0.0.1:18000/imagenet/prediction"
start = time.time()
with open(os.path.join(".", "daisy.jpg"), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
keys, values = [], []
for i in range(batch_size):
keys.append("image_{}".format(i))
values.append(image)
data = {"key": keys, "value": values}
latency_list = []
start_time = time.time()
total_num = 0
while True:
l_start = time.time()
r = requests.post(url=url, data=json.dumps(data))
print(r.json())
l_end = time.time()
latency_list.append(l_end * 1000 - l_start * 1000)
total_num += 1
if time.time() - start_time > 20:
break
end = time.time()
return [[end - start], latency_list, [total_num]]
def multithread_http(thread, batch_size):
multi_thread_runner = MultiThreadRunner()
start = time.time()
result = multi_thread_runner.run(run_http, thread, batch_size)
end = time.time()
total_cost = end - start
avg_cost = 0
total_number = 0
for i in range(thread):
avg_cost += result[0][i]
total_number += result[2][i]
avg_cost = avg_cost / thread
print("Total cost: {}s".format(total_cost))
print("Each thread cost: {}s. ".format(avg_cost))
print("Total count: {}. ".format(total_number))
print("AVG QPS: {} samples/s".format(batch_size * total_number /
total_cost))
show_latency(result[1])
def run_rpc(thread, batch_size):
client = PipelineClient()
client.connect(['127.0.0.1:18080'])
start = time.time()
test_img_dir = "imgs/"
for img_file in os.listdir(test_img_dir):
with open(os.path.join(test_img_dir, img_file), 'rb') as file:
image_data = file.read()
image = cv2_to_base64(image_data)
start_time = time.time()
while True:
ret = client.predict(feed_dict={"image": image}, fetch=["res"])
if time.time() - start_time > 10:
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]
if device == "gpu":
gpu_id = sys.argv[5]
else:
gpu_id = None
gen_yml(device, gpu_id)
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/PaddleClas/ResNet_V2_50/benchmark.sh 0 → 100644
浏览文件 @ e9ecf97c
export FLAGS_profile_pipeline=1
alias python3="python3.6"
modelname="clas-ResNet_v2_50"
# HTTP
#ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3
# Create yaml，If you already have the config.yaml, ignore it.
#python3 benchmark.py yaml local_predictor 1 gpu
rm -rf profile_log_$modelname
echo "Starting HTTP Clients..."
# Start a client in each thread, tesing the case of multiple threads.
for thread_num in 1 2 4 8 12 16
do
for batch_size in 1
do
echo "----${modelname} thread num: ${thread_num} batch size: ${batch_size} mode:http ----" >>profile_log_$modelname
# Start one web service, If you start the service yourself, you can ignore it here.
#python3 web_service.py >web.log 2>&1 &
#sleep 3
# --id is the serial number of the GPU card, Must be the same as the gpu id used by the server.
nvidia-smi --id=3 --query-gpu=memory.used --format=csv -lms 1000 > gpu_use.log 2>&1 &
nvidia-smi --id=3 --query-gpu=utilization.gpu --format=csv -lms 1000 > gpu_utilization.log 2>&1 &
echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
# Start http client
python3 benchmark.py run http $thread_num $batch_size > profile 2>&1
# Collect CPU metrics, Filter data that is zero momentarily, Record the maximum value of GPU memory and the average value of GPU utilization
python3 cpu_utilization.py >> profile_log_$modelname
grep -av '^0 %' gpu_utilization.log > gpu_utilization.log.tmp
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 -F' ' '{sum+=$1} END {print "GPU_UTILIZATION:", sum/NR, sum, NR }' gpu_utilization.log.tmp >> profile_log_$modelname
# Show profiles
python3 ../../../util/show_profile.py profile $thread_num >> profile_log_$modelname
tail -n 8 profile >> profile_log_$modelname
echo '' >> profile_log_$modelname
done
done
# Kill all nvidia-smi background task.
pkill nvidia-smi
python/examples/pipeline/PaddleClas/ResNet_V2_50/config.yml 0 → 100644
浏览文件 @ e9ecf97c
#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: 18080
rpc_port: 9993
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: 1
#uci模型路径
model_config: resnet_v2_50_imagenet_model/
#计算硬件类型: 空缺时由devices决定(CPU/GPU)，0=cpu, 1=gpu, 2=tensorRT, 3=arm cpu, 4=kunlun xpu
device_type: 1
#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/PaddleClas/ResNet_V2_50/pipeline_http_client.py 0 → 100644
浏览文件 @ e9ecf97c
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')
if __name__ == "__main__":
url = "http://127.0.0.1:18000/imagenet/prediction"
with open(os.path.join(".", "daisy.jpg"), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
data = {"key": ["image"], "value": [image]}
for i in range(1):
r = requests.post(url=url, data=json.dumps(data))
print(r.json())
python/examples/pipeline/PaddleClas/ResNet_V2_50/resnet50_web_service.py 0 → 100644
浏览文件 @ e9ecf97c
# 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
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()
batch_size = len(input_dict.keys())
imgs = []
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)
img = self.seq(im)
imgs.append(img[np.newaxis, :].copy())
input_imgs = np.concatenate(imgs, axis=0)
return {"image": input_imgs}, False, None, ""
def postprocess(self, input_dicts, fetch_dict, log_id):
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/pipeline/PaddleClas/ShuffleNetV2_x1_0/README.md 0 → 100644
浏览文件 @ e9ecf97c
# 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/PaddleClas/ShuffleNetV2_x1_0/README_CN.md 0 → 100644
浏览文件 @ e9ecf97c
# Imagenet Pipeline WebService
这里以 Imagenet 服务为例来介绍 Pipeline WebService 的使用。
## 获取模型
```
sh get_model.sh
```
## 启动服务
```
python resnet50_web_service.py &>log.txt &
```
## 测试
```
python pipeline_rpc_client.py
```
python/examples/pipeline/PaddleClas/ShuffleNetV2_x1_0/benchmark.py 0 → 100644
浏览文件 @ e9ecf97c
import sys
import os
import base64
import yaml
import requests
import time
import json
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 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, gpu_id):
fin = open("config.yml", "r")
config = yaml.load(fin)
fin.close()
config["dag"]["tracer"] = {"interval_s": 10}
if device == "gpu":
config["op"]["imagenet"]["local_service_conf"]["device_type"] = 1
config["op"]["imagenet"]["local_service_conf"]["devices"] = gpu_id
else:
config["op"]["imagenet"]["local_service_conf"]["device_type"] = 0
with open("config2.yml", "w") as fout:
yaml.dump(config, fout, default_flow_style=False)
def cv2_to_base64(image):
return base64.b64encode(image).decode('utf8')
def run_http(idx, batch_size):
print("start thread ({})".format(idx))
url = "http://127.0.0.1:18080/imagenet/prediction"
start = time.time()
with open(os.path.join(".", "daisy.jpg"), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
keys, values = [], []
for i in range(batch_size):
keys.append("image_{}".format(i))
values.append(image)
data = {"key": keys, "value": values}
latency_list = []
start_time = time.time()
total_num = 0
while True:
l_start = time.time()
r = requests.post(url=url, data=json.dumps(data))
print(r.json())
l_end = time.time()
latency_list.append(l_end * 1000 - l_start * 1000)
total_num += 1
if time.time() - start_time > 20:
break
end = time.time()
return [[end - start], latency_list, [total_num]]
def multithread_http(thread, batch_size):
multi_thread_runner = MultiThreadRunner()
start = time.time()
result = multi_thread_runner.run(run_http, thread, batch_size)
end = time.time()
total_cost = end - start
avg_cost = 0
total_number = 0
for i in range(thread):
avg_cost += result[0][i]
total_number += result[2][i]
avg_cost = avg_cost / thread
print("Total cost: {}s".format(total_cost))
print("Each thread cost: {}s. ".format(avg_cost))
print("Total count: {}. ".format(total_number))
print("AVG QPS: {} samples/s".format(batch_size * total_number /
total_cost))
show_latency(result[1])
def run_rpc(thread, batch_size):
client = PipelineClient()
client.connect(['127.0.0.1:18080'])
start = time.time()
test_img_dir = "imgs/"
for img_file in os.listdir(test_img_dir):
with open(os.path.join(test_img_dir, img_file), 'rb') as file:
image_data = file.read()
image = cv2_to_base64(image_data)
start_time = time.time()
while True:
ret = client.predict(feed_dict={"image": image}, fetch=["res"])
if time.time() - start_time > 10:
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]
if device == "gpu":
gpu_id = sys.argv[5]
else:
gpu_id = None
gen_yml(device, gpu_id)
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/PaddleClas/ShuffleNetV2_x1_0/benchmark.sh 0 → 100644
浏览文件 @ e9ecf97c
export FLAGS_profile_pipeline=1
alias python3="python3.6"
modelname="clas-ShuffleNetV2_x1_0"
# HTTP
#ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3
# Create yaml，If you already have the config.yaml, ignore it.
#python3 benchmark.py yaml local_predictor 1 gpu
rm -rf profile_log_$modelname
echo "Starting HTTP Clients..."
# Start a client in each thread, tesing the case of multiple threads.
for thread_num in 1 2 4 8 12 16
do
for batch_size in 1
do
echo "----${modelname} thread num: ${thread_num} batch size: ${batch_size} mode:http ----" >>profile_log_$modelname
# Start one web service, If you start the service yourself, you can ignore it here.
#python3 web_service.py >web.log 2>&1 &
#sleep 3
# --id is the serial number of the GPU card, Must be the same as the gpu id used by the server.
nvidia-smi --id=3 --query-gpu=memory.used --format=csv -lms 1000 > gpu_use.log 2>&1 &
nvidia-smi --id=3 --query-gpu=utilization.gpu --format=csv -lms 1000 > gpu_utilization.log 2>&1 &
echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
# Start http client
python3 benchmark.py run http $thread_num $batch_size > profile 2>&1
# Collect CPU metrics, Filter data that is zero momentarily, Record the maximum value of GPU memory and the average value of GPU utilization
python3 cpu_utilization.py >> profile_log_$modelname
grep -av '^0 %' gpu_utilization.log > gpu_utilization.log.tmp
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 -F' ' '{sum+=$1} END {print "GPU_UTILIZATION:", sum/NR, sum, NR }' gpu_utilization.log.tmp >> profile_log_$modelname
# Show profiles
python3 ../../../util/show_profile.py profile $thread_num >> profile_log_$modelname
tail -n 8 profile >> profile_log_$modelname
echo '' >> profile_log_$modelname
done
done
# Kill all nvidia-smi background task.
pkill nvidia-smi
python/examples/pipeline/PaddleClas/ShuffleNetV2_x1_0/benchmark_config.yaml 0 → 100644
浏览文件 @ e9ecf97c
cuda_version: "10.1"
cudnn_version: "7.6"
trt_version: "6.0"
python_version: "3.7"
gcc_version: "8.2"
paddle_version: "2.0.1"
cpu: "Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz X12"
gpu: "T4"
xpu: "None"
api: ""
owner: "cuicheng01"
model_name: "ShuffleNetV2_x1_0"
model_type: "static"
model_source: "PaddleClas"
model_url: "https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_x1_0_pretrained.tar"
batch_size: 1
num_of_samples: 1000
input_shape: "3,224,224"
runtime_device: "gpu"
ir_optim: true
enable_memory_optim: true
enable_tensorrt: false
precision: "fp32"
enable_mkldnn: false
cpu_math_library_num_threads: ""
python/examples/pipeline/PaddleClas/ShuffleNetV2_x1_0/benchmark_config.yaml.template 0 → 100644
浏览文件 @ e9ecf97c
cuda_version: "10.1"
cudnn_version: "7.6"
trt_version: "6.0"
python_version: "3.7"
gcc_version: "8.2"
paddle_version: "2.0.1"
cpu: "Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz X12"
gpu: "T4"
xpu: "None"
api: ""
owner: "cuicheng01"
model_name: "imagenet"
model_type: "static"
model_source: "PaddleClas"
model_url: "model_url_path"
batch_size: 1
num_of_samples: 1000
input_shape: "3,224,224"
runtime_device: "cpu"
ir_optim: true
enable_memory_optim: true
enable_tensorrt: false
precision: "fp32"
enable_mkldnn: false
cpu_math_library_num_threads: ""
python/examples/pipeline/PaddleClas/ShuffleNetV2_x1_0/benchmark_gpu.sh 0 → 100644
浏览文件 @ e9ecf97c
export FLAGS_profile_pipeline=1
alias python3="python3.7"
modelname="imagenet"
use_gpu=1
gpu_id="0"
benchmark_config_filename="benchmark_config.yaml"
# HTTP
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3
if [ $use_gpu -eq 1 ]; then
python3 benchmark.py yaml local_predictor 1 gpu $gpu_id
else
python3 benchmark.py yaml local_predictor 1 cpu
fi
rm -rf profile_log_$modelname
for thread_num in 1
do
for batch_size in 1
do
echo "#----imagenet thread num: $thread_num batch size: $batch_size mode:http use_gpu:$use_gpu----" >>profile_log_$modelname
rm -rf PipelineServingLogs
rm -rf cpu_utilization.py
python3 resnet50_web_service.py >web.log 2>&1 &
sleep 3
nvidia-smi --id=${gpu_id} --query-compute-apps=used_memory --format=csv -lms 100 > gpu_use.log 2>&1 &
nvidia-smi --id=${gpu_id} --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
python3 -m paddle_serving_server_gpu.profiler >>profile_log_$modelname
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
ps -ef | grep nvidia-smi | 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=$modelname}} END {print "GPU_MEM:", max}' gpu_use.log >> profile_log_$modelname
awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "GPU_UTIL:", max}' gpu_utilization.log >> profile_log_$modelname
cat benchmark.log >> profile_log_$modelname
python3 -m paddle_serving_server_gpu.parse_profile --benchmark_cfg $benchmark_config_filename --benchmark_log profile_log_$modelname
#rm -rf gpu_use.log gpu_utilization.log
done
done
python/examples/pipeline/PaddleClas/ShuffleNetV2_x1_0/config.yml 0 → 100644
浏览文件 @ e9ecf97c
#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: 18080
rpc_port: 9993
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: 1
#uci模型路径
model_config: ShuffleNetV2_x1_0/ppcls_model/
#计算硬件类型: 空缺时由devices决定(CPU/GPU)，0=cpu, 1=gpu, 2=tensorRT, 3=arm cpu, 4=kunlun xpu
device_type: 1
#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: ["prediction"]
python/examples/pipeline/PaddleClas/ShuffleNetV2_x1_0/cpu_utilization.py 0 → 100644
浏览文件 @ e9ecf97c
import psutil
cpu_utilization=psutil.cpu_percent(1,False)
print('CPU_UTILIZATION:', cpu_utilization)
python/examples/pipeline/PaddleClas/ShuffleNetV2_x1_0/get_model.sh 0 → 100644
浏览文件 @ e9ecf97c
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/model/ShuffleNetV2_x1_0.tar
tar -xf ShuffleNetV2_x1_0.tar
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/PaddleClas/ShuffleNetV2_x1_0/imagenet.label 0 → 100644
浏览文件 @ e9ecf97c
tench, Tinca tinca,
goldfish, Carassius auratus,
great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias,
tiger shark, Galeocerdo cuvieri,
hammerhead, hammerhead shark,
electric ray, crampfish, numbfish, torpedo,
stingray,
cock,
hen,
ostrich, Struthio camelus,
brambling, Fringilla montifringilla,
goldfinch, Carduelis carduelis,
house finch, linnet, Carpodacus mexicanus,
junco, snowbird,
indigo bunting, indigo finch, indigo bird, Passerina cyanea,
robin, American robin, Turdus migratorius,
bulbul,
jay,
magpie,
chickadee,
water ouzel, dipper,
kite,
bald eagle, American eagle, Haliaeetus leucocephalus,
vulture,
great grey owl, great gray owl, Strix nebulosa,
European fire salamander, Salamandra salamandra,
common newt, Triturus vulgaris,
eft,
spotted salamander, Ambystoma maculatum,
axolotl, mud puppy, Ambystoma mexicanum,
bullfrog, Rana catesbeiana,
tree frog, tree-frog,
tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui,
loggerhead, loggerhead turtle, Caretta caretta,
leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea,
mud turtle,
terrapin,
box turtle, box tortoise,
banded gecko,
common iguana, iguana, Iguana iguana,
American chameleon, anole, Anolis carolinensis,
whiptail, whiptail lizard,
agama,
frilled lizard, Chlamydosaurus kingi,
alligator lizard,
Gila monster, Heloderma suspectum,
green lizard, Lacerta viridis,
African chameleon, Chamaeleo chamaeleon,
Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis,
African crocodile, Nile crocodile, Crocodylus niloticus,
American alligator, Alligator mississipiensis,
triceratops,
thunder snake, worm snake, Carphophis amoenus,
ringneck snake, ring-necked snake, ring snake,
hognose snake, puff adder, sand viper,
green snake, grass snake,
king snake, kingsnake,
garter snake, grass snake,
water snake,
vine snake,
night snake, Hypsiglena torquata,
boa constrictor, Constrictor constrictor,
rock python, rock snake, Python sebae,
Indian cobra, Naja naja,
green mamba,
sea snake,
horned viper, cerastes, sand viper, horned asp, Cerastes cornutus,
diamondback, diamondback rattlesnake, Crotalus adamanteus,
sidewinder, horned rattlesnake, Crotalus cerastes,
trilobite,
harvestman, daddy longlegs, Phalangium opilio,
scorpion,
black and gold garden spider, Argiope aurantia,
barn spider, Araneus cavaticus,
garden spider, Aranea diademata,
black widow, Latrodectus mactans,
tarantula,
wolf spider, hunting spider,
tick,
centipede,
black grouse,
ptarmigan,
ruffed grouse, partridge, Bonasa umbellus,
prairie chicken, prairie grouse, prairie fowl,
peacock,
quail,
partridge,
African grey, African gray, Psittacus erithacus,
macaw,
sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita,
lorikeet,
coucal,
bee eater,
hornbill,
hummingbird,
jacamar,
toucan,
drake,
red-breasted merganser, Mergus serrator,
goose,
black swan, Cygnus atratus,
tusker,
echidna, spiny anteater, anteater,
platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus,
wallaby, brush kangaroo,
koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus,
wombat,
jellyfish,
sea anemone, anemone,
brain coral,
flatworm, platyhelminth,
nematode, nematode worm, roundworm,
conch,
snail,
slug,
sea slug, nudibranch,
chiton, coat-of-mail shell, sea cradle, polyplacophore,
chambered nautilus, pearly nautilus, nautilus,
Dungeness crab, Cancer magister,
rock crab, Cancer irroratus,
fiddler crab,
king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica,
American lobster, Northern lobster, Maine lobster, Homarus americanus,
spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish,
crayfish, crawfish, crawdad, crawdaddy,
hermit crab,
isopod,
white stork, Ciconia ciconia,
black stork, Ciconia nigra,
spoonbill,
flamingo,
little blue heron, Egretta caerulea,
American egret, great white heron, Egretta albus,
bittern,
crane,
limpkin, Aramus pictus,
European gallinule, Porphyrio porphyrio,
American coot, marsh hen, mud hen, water hen, Fulica americana,
bustard,
ruddy turnstone, Arenaria interpres,
red-backed sandpiper, dunlin, Erolia alpina,
redshank, Tringa totanus,
dowitcher,
oystercatcher, oyster catcher,
pelican,
king penguin, Aptenodytes patagonica,
albatross, mollymawk,
grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus,
killer whale, killer, orca, grampus, sea wolf, Orcinus orca,
dugong, Dugong dugon,
sea lion,
Chihuahua,
Japanese spaniel,
Maltese dog, Maltese terrier, Maltese,
Pekinese, Pekingese, Peke,
Shih-Tzu,
Blenheim spaniel,
papillon,
toy terrier,
Rhodesian ridgeback,
Afghan hound, Afghan,
basset, basset hound,
beagle,
bloodhound, sleuthhound,
bluetick,
black-and-tan coonhound,
Walker hound, Walker foxhound,
English foxhound,
redbone,
borzoi, Russian wolfhound,
Irish wolfhound,
Italian greyhound,
whippet,
Ibizan hound, Ibizan Podenco,
Norwegian elkhound, elkhound,
otterhound, otter hound,
Saluki, gazelle hound,
Scottish deerhound, deerhound,
Weimaraner,
Staffordshire bullterrier, Staffordshire bull terrier,
American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier,
Bedlington terrier,
Border terrier,
Kerry blue terrier,
Irish terrier,
Norfolk terrier,
Norwich terrier,
Yorkshire terrier,
wire-haired fox terrier,
Lakeland terrier,
Sealyham terrier, Sealyham,
Airedale, Airedale terrier,
cairn, cairn terrier,
Australian terrier,
Dandie Dinmont, Dandie Dinmont terrier,
Boston bull, Boston terrier,
miniature schnauzer,
giant schnauzer,
standard schnauzer,
Scotch terrier, Scottish terrier, Scottie,
Tibetan terrier, chrysanthemum dog,
silky terrier, Sydney silky,
soft-coated wheaten terrier,
West Highland white terrier,
Lhasa, Lhasa apso,
flat-coated retriever,
curly-coated retriever,
golden retriever,
Labrador retriever,
Chesapeake Bay retriever,
German short-haired pointer,
vizsla, Hungarian pointer,
English setter,
Irish setter, red setter,
Gordon setter,
Brittany spaniel,
clumber, clumber spaniel,
English springer, English springer spaniel,
Welsh springer spaniel,
cocker spaniel, English cocker spaniel, cocker,
Sussex spaniel,
Irish water spaniel,
kuvasz,
schipperke,
groenendael,
malinois,
briard,
kelpie,
komondor,
Old English sheepdog, bobtail,
Shetland sheepdog, Shetland sheep dog, Shetland,
collie,
Border collie,
Bouvier des Flandres, Bouviers des Flandres,
Rottweiler,
German shepherd, German shepherd dog, German police dog, alsatian,
Doberman, Doberman pinscher,
miniature pinscher,
Greater Swiss Mountain dog,
Bernese mountain dog,
Appenzeller,
EntleBucher,
boxer,
bull mastiff,
Tibetan mastiff,
French bulldog,
Great Dane,
Saint Bernard, St Bernard,
Eskimo dog, husky,
malamute, malemute, Alaskan malamute,
Siberian husky,
dalmatian, coach dog, carriage dog,
affenpinscher, monkey pinscher, monkey dog,
basenji,
pug, pug-dog,
Leonberg,
Newfoundland, Newfoundland dog,
Great Pyrenees,
Samoyed, Samoyede,
Pomeranian,
chow, chow chow,
keeshond,
Brabancon griffon,
Pembroke, Pembroke Welsh corgi,
Cardigan, Cardigan Welsh corgi,
toy poodle,
miniature poodle,
standard poodle,
Mexican hairless,
timber wolf, grey wolf, gray wolf, Canis lupus,
white wolf, Arctic wolf, Canis lupus tundrarum,
red wolf, maned wolf, Canis rufus, Canis niger,
coyote, prairie wolf, brush wolf, Canis latrans,
dingo, warrigal, warragal, Canis dingo,
dhole, Cuon alpinus,
African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus,
hyena, hyaena,
red fox, Vulpes vulpes,
kit fox, Vulpes macrotis,
Arctic fox, white fox, Alopex lagopus,
grey fox, gray fox, Urocyon cinereoargenteus,
tabby, tabby cat,
tiger cat,
Persian cat,
Siamese cat, Siamese,
Egyptian cat,
cougar, puma, catamount, mountain lion, painter, panther, Felis concolor,
lynx, catamount,
leopard, Panthera pardus,
snow leopard, ounce, Panthera uncia,
jaguar, panther, Panthera onca, Felis onca,
lion, king of beasts, Panthera leo,
tiger, Panthera tigris,
cheetah, chetah, Acinonyx jubatus,
brown bear, bruin, Ursus arctos,
American black bear, black bear, Ursus americanus, Euarctos americanus,
ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus,
sloth bear, Melursus ursinus, Ursus ursinus,
mongoose,
meerkat, mierkat,
tiger beetle,
ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle,
ground beetle, carabid beetle,
long-horned beetle, longicorn, longicorn beetle,
leaf beetle, chrysomelid,
dung beetle,
rhinoceros beetle,
weevil,
fly,
bee,
ant, emmet, pismire,
grasshopper, hopper,
cricket,
walking stick, walkingstick, stick insect,
cockroach, roach,
mantis, mantid,
cicada, cicala,
leafhopper,
lacewing, lacewing fly,
"dragonfly, darning needle, devils darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
damselfly,
admiral,
ringlet, ringlet butterfly,
monarch, monarch butterfly, milkweed butterfly, Danaus plexippus,
cabbage butterfly,
sulphur butterfly, sulfur butterfly,
lycaenid, lycaenid butterfly,
starfish, sea star,
sea urchin,
sea cucumber, holothurian,
wood rabbit, cottontail, cottontail rabbit,
hare,
Angora, Angora rabbit,
hamster,
porcupine, hedgehog,
fox squirrel, eastern fox squirrel, Sciurus niger,
marmot,
beaver,
guinea pig, Cavia cobaya,
sorrel,
zebra,
hog, pig, grunter, squealer, Sus scrofa,
wild boar, boar, Sus scrofa,
warthog,
hippopotamus, hippo, river horse, Hippopotamus amphibius,
ox,
water buffalo, water ox, Asiatic buffalo, Bubalus bubalis,
bison,
ram, tup,
bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis,
ibex, Capra ibex,
hartebeest,
impala, Aepyceros melampus,
gazelle,
Arabian camel, dromedary, Camelus dromedarius,
llama,
weasel,
mink,
polecat, fitch, foulmart, foumart, Mustela putorius,
black-footed ferret, ferret, Mustela nigripes,
otter,
skunk, polecat, wood pussy,
badger,
armadillo,
three-toed sloth, ai, Bradypus tridactylus,
orangutan, orang, orangutang, Pongo pygmaeus,
gorilla, Gorilla gorilla,
chimpanzee, chimp, Pan troglodytes,
gibbon, Hylobates lar,
siamang, Hylobates syndactylus, Symphalangus syndactylus,
guenon, guenon monkey,
patas, hussar monkey, Erythrocebus patas,
baboon,
macaque,
langur,
colobus, colobus monkey,
proboscis monkey, Nasalis larvatus,
marmoset,
capuchin, ringtail, Cebus capucinus,
howler monkey, howler,
titi, titi monkey,
spider monkey, Ateles geoffroyi,
squirrel monkey, Saimiri sciureus,
Madagascar cat, ring-tailed lemur, Lemur catta,
indri, indris, Indri indri, Indri brevicaudatus,
Indian elephant, Elephas maximus,
African elephant, Loxodonta africana,
lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens,
giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca,
barracouta, snoek,
eel,
coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch,
rock beauty, Holocanthus tricolor,
anemone fish,
sturgeon,
gar, garfish, garpike, billfish, Lepisosteus osseus,
lionfish,
puffer, pufferfish, blowfish, globefish,
abacus,
abaya,
"academic gown, academic robe, judges robe",
accordion, piano accordion, squeeze box,
acoustic guitar,
aircraft carrier, carrier, flattop, attack aircraft carrier,
airliner,
airship, dirigible,
altar,
ambulance,
amphibian, amphibious vehicle,
analog clock,
apiary, bee house,
apron,
ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin,
assault rifle, assault gun,
backpack, back pack, knapsack, packsack, rucksack, haversack,
bakery, bakeshop, bakehouse,
balance beam, beam,
balloon,
ballpoint, ballpoint pen, ballpen, Biro,
Band Aid,
banjo,
bannister, banister, balustrade, balusters, handrail,
barbell,
barber chair,
barbershop,
barn,
barometer,
barrel, cask,
barrow, garden cart, lawn cart, wheelbarrow,
baseball,
basketball,
bassinet,
bassoon,
bathing cap, swimming cap,
bath towel,
bathtub, bathing tub, bath, tub,
beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon,
beacon, lighthouse, beacon light, pharos,
beaker,
bearskin, busby, shako,
beer bottle,
beer glass,
bell cote, bell cot,
bib,
bicycle-built-for-two, tandem bicycle, tandem,
bikini, two-piece,
binder, ring-binder,
binoculars, field glasses, opera glasses,
birdhouse,
boathouse,
bobsled, bobsleigh, bob,
bolo tie, bolo, bola tie, bola,
bonnet, poke bonnet,
bookcase,
bookshop, bookstore, bookstall,
bottlecap,
bow,
bow tie, bow-tie, bowtie,
brass, memorial tablet, plaque,
brassiere, bra, bandeau,
breakwater, groin, groyne, mole, bulwark, seawall, jetty,
breastplate, aegis, egis,
broom,
bucket, pail,
buckle,
bulletproof vest,
bullet train, bullet,
butcher shop, meat market,
cab, hack, taxi, taxicab,
caldron, cauldron,
candle, taper, wax light,
cannon,
canoe,
can opener, tin opener,
cardigan,
car mirror,
carousel, carrousel, merry-go-round, roundabout, whirligig,
"carpenters kit, tool kit",
carton,
car wheel,
cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM,
cassette,
cassette player,
castle,
catamaran,
CD player,
cello, violoncello,
cellular telephone, cellular phone, cellphone, cell, mobile phone,
chain,
chainlink fence,
chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour,
chain saw, chainsaw,
chest,
chiffonier, commode,
chime, bell, gong,
china cabinet, china closet,
Christmas stocking,
church, church building,
cinema, movie theater, movie theatre, movie house, picture palace,
cleaver, meat cleaver, chopper,
cliff dwelling,
cloak,
clog, geta, patten, sabot,
cocktail shaker,
coffee mug,
coffeepot,
coil, spiral, volute, whorl, helix,
combination lock,
computer keyboard, keypad,
confectionery, confectionary, candy store,
container ship, containership, container vessel,
convertible,
corkscrew, bottle screw,
cornet, horn, trumpet, trump,
cowboy boot,
cowboy hat, ten-gallon hat,
cradle,
crane,
crash helmet,
crate,
crib, cot,
Crock Pot,
croquet ball,
crutch,
cuirass,
dam, dike, dyke,
desk,
desktop computer,
dial telephone, dial phone,
diaper, nappy, napkin,
digital clock,
digital watch,
dining table, board,
dishrag, dishcloth,
dishwasher, dish washer, dishwashing machine,
disk brake, disc brake,
dock, dockage, docking facility,
dogsled, dog sled, dog sleigh,
dome,
doormat, welcome mat,
drilling platform, offshore rig,
drum, membranophone, tympan,
drumstick,
dumbbell,
Dutch oven,
electric fan, blower,
electric guitar,
electric locomotive,
entertainment center,
envelope,
espresso maker,
face powder,
feather boa, boa,
file, file cabinet, filing cabinet,
fireboat,
fire engine, fire truck,
fire screen, fireguard,
flagpole, flagstaff,
flute, transverse flute,
folding chair,
football helmet,
forklift,
fountain,
fountain pen,
four-poster,
freight car,
French horn, horn,
frying pan, frypan, skillet,
fur coat,
garbage truck, dustcart,
gasmask, respirator, gas helmet,
gas pump, gasoline pump, petrol pump, island dispenser,
goblet,
go-kart,
golf ball,
golfcart, golf cart,
gondola,
gong, tam-tam,
gown,
grand piano, grand,
greenhouse, nursery, glasshouse,
grille, radiator grille,
grocery store, grocery, food market, market,
guillotine,
hair slide,
hair spray,
half track,
hammer,
hamper,
hand blower, blow dryer, blow drier, hair dryer, hair drier,
hand-held computer, hand-held microcomputer,
handkerchief, hankie, hanky, hankey,
hard disc, hard disk, fixed disk,
harmonica, mouth organ, harp, mouth harp,
harp,
harvester, reaper,
hatchet,
holster,
home theater, home theatre,
honeycomb,
hook, claw,
hoopskirt, crinoline,
horizontal bar, high bar,
horse cart, horse-cart,
hourglass,
iPod,
iron, smoothing iron,
"jack-o-lantern",
jean, blue jean, denim,
jeep, landrover,
jersey, T-shirt, tee shirt,
jigsaw puzzle,
jinrikisha, ricksha, rickshaw,
joystick,
kimono,
knee pad,
knot,
lab coat, laboratory coat,
ladle,
lampshade, lamp shade,
laptop, laptop computer,
lawn mower, mower,
lens cap, lens cover,
letter opener, paper knife, paperknife,
library,
lifeboat,
lighter, light, igniter, ignitor,
limousine, limo,
liner, ocean liner,
lipstick, lip rouge,
Loafer,
lotion,
loudspeaker, speaker, speaker unit, loudspeaker system, speaker system,
"loupe, jewelers loupe",
lumbermill, sawmill,
magnetic compass,
mailbag, postbag,
mailbox, letter box,
maillot,
maillot, tank suit,
manhole cover,
maraca,
marimba, xylophone,
mask,
matchstick,
maypole,
maze, labyrinth,
measuring cup,
medicine chest, medicine cabinet,
megalith, megalithic structure,
microphone, mike,
microwave, microwave oven,
military uniform,
milk can,
minibus,
miniskirt, mini,
minivan,
missile,
mitten,
mixing bowl,
mobile home, manufactured home,
Model T,
modem,
monastery,
monitor,
moped,
mortar,
mortarboard,
mosque,
mosquito net,
motor scooter, scooter,
mountain bike, all-terrain bike, off-roader,
mountain tent,
mouse, computer mouse,
mousetrap,
moving van,
muzzle,
nail,
neck brace,
necklace,
nipple,
notebook, notebook computer,
obelisk,
oboe, hautboy, hautbois,
ocarina, sweet potato,
odometer, hodometer, mileometer, milometer,
oil filter,
organ, pipe organ,
oscilloscope, scope, cathode-ray oscilloscope, CRO,
overskirt,
oxcart,
oxygen mask,
packet,
paddle, boat paddle,
paddlewheel, paddle wheel,
padlock,
paintbrush,
"pajama, pyjama, pjs, jammies",
palace,
panpipe, pandean pipe, syrinx,
paper towel,
parachute, chute,
parallel bars, bars,
park bench,
parking meter,
passenger car, coach, carriage,
patio, terrace,
pay-phone, pay-station,
pedestal, plinth, footstall,
pencil box, pencil case,
pencil sharpener,
perfume, essence,
Petri dish,
photocopier,
pick, plectrum, plectron,
pickelhaube,
picket fence, paling,
pickup, pickup truck,
pier,
piggy bank, penny bank,
pill bottle,
pillow,
ping-pong ball,
pinwheel,
pirate, pirate ship,
pitcher, ewer,
"plane, carpenters plane, woodworking plane",
planetarium,
plastic bag,
plate rack,
plow, plough,
"plunger, plumbers helper",
Polaroid camera, Polaroid Land camera,
pole,
police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria,
poncho,
pool table, billiard table, snooker table,
pop bottle, soda bottle,
pot, flowerpot,
"potters wheel",
power drill,
prayer rug, prayer mat,
printer,
prison, prison house,
projectile, missile,
projector,
puck, hockey puck,
punching bag, punch bag, punching ball, punchball,
purse,
quill, quill pen,
quilt, comforter, comfort, puff,
racer, race car, racing car,
racket, racquet,
radiator,
radio, wireless,
radio telescope, radio reflector,
rain barrel,
recreational vehicle, RV, R.V.,
reel,
reflex camera,
refrigerator, icebox,
remote control, remote,
restaurant, eating house, eating place, eatery,
revolver, six-gun, six-shooter,
rifle,
rocking chair, rocker,
rotisserie,
rubber eraser, rubber, pencil eraser,
rugby ball,
rule, ruler,
running shoe,
safe,
safety pin,
saltshaker, salt shaker,
sandal,
sarong,
sax, saxophone,
scabbard,
scale, weighing machine,
school bus,
schooner,
scoreboard,
screen, CRT screen,
screw,
screwdriver,
seat belt, seatbelt,
sewing machine,
shield, buckler,
shoe shop, shoe-shop, shoe store,
shoji,
shopping basket,
shopping cart,
shovel,
shower cap,
shower curtain,
ski,
ski mask,
sleeping bag,
slide rule, slipstick,
sliding door,
slot, one-armed bandit,
snorkel,
snowmobile,
snowplow, snowplough,
soap dispenser,
soccer ball,
sock,
solar dish, solar collector, solar furnace,
sombrero,
soup bowl,
space bar,
space heater,
space shuttle,
spatula,
speedboat,
"spider web, spiders web",
spindle,
sports car, sport car,
spotlight, spot,
stage,
steam locomotive,
steel arch bridge,
steel drum,
stethoscope,
stole,
stone wall,
stopwatch, stop watch,
stove,
strainer,
streetcar, tram, tramcar, trolley, trolley car,
stretcher,
studio couch, day bed,
stupa, tope,
submarine, pigboat, sub, U-boat,
suit, suit of clothes,
sundial,
sunglass,
sunglasses, dark glasses, shades,
sunscreen, sunblock, sun blocker,
suspension bridge,
swab, swob, mop,
sweatshirt,
swimming trunks, bathing trunks,
swing,
switch, electric switch, electrical switch,
syringe,
table lamp,
tank, army tank, armored combat vehicle, armoured combat vehicle,
tape player,
teapot,
teddy, teddy bear,
television, television system,
tennis ball,
thatch, thatched roof,
theater curtain, theatre curtain,
thimble,
thresher, thrasher, threshing machine,
throne,
tile roof,
toaster,
tobacco shop, tobacconist shop, tobacconist,
toilet seat,
torch,
totem pole,
tow truck, tow car, wrecker,
toyshop,
tractor,
trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi,
tray,
trench coat,
tricycle, trike, velocipede,
trimaran,
tripod,
triumphal arch,
trolleybus, trolley coach, trackless trolley,
trombone,
tub, vat,
turnstile,
typewriter keyboard,
umbrella,
unicycle, monocycle,
upright, upright piano,
vacuum, vacuum cleaner,
vase,
vault,
velvet,
vending machine,
vestment,
viaduct,
violin, fiddle,
volleyball,
waffle iron,
wall clock,
wallet, billfold, notecase, pocketbook,
wardrobe, closet, press,
warplane, military plane,
washbasin, handbasin, washbowl, lavabo, wash-hand basin,
washer, automatic washer, washing machine,
water bottle,
water jug,
water tower,
whiskey jug,
whistle,
wig,
window screen,
window shade,
Windsor tie,
wine bottle,
wing,
wok,
wooden spoon,
wool, woolen, woollen,
worm fence, snake fence, snake-rail fence, Virginia fence,
wreck,
yawl,
yurt,
web site, website, internet site, site,
comic book,
crossword puzzle, crossword,
street sign,
traffic light, traffic signal, stoplight,
book jacket, dust cover, dust jacket, dust wrapper,
menu,
plate,
guacamole,
consomme,
hot pot, hotpot,
trifle,
ice cream, icecream,
ice lolly, lolly, lollipop, popsicle,
French loaf,
bagel, beigel,
pretzel,
cheeseburger,
hotdog, hot dog, red hot,
mashed potato,
head cabbage,
broccoli,
cauliflower,
zucchini, courgette,
spaghetti squash,
acorn squash,
butternut squash,
cucumber, cuke,
artichoke, globe artichoke,
bell pepper,
cardoon,
mushroom,
Granny Smith,
strawberry,
orange,
lemon,
fig,
pineapple, ananas,
banana,
jackfruit, jak, jack,
custard apple,
pomegranate,
hay,
carbonara,
chocolate sauce, chocolate syrup,
dough,
meat loaf, meatloaf,
pizza, pizza pie,
potpie,
burrito,
red wine,
espresso,
cup,
eggnog,
alp,
bubble,
cliff, drop, drop-off,
coral reef,
geyser,
lakeside, lakeshore,
promontory, headland, head, foreland,
sandbar, sand bar,
seashore, coast, seacoast, sea-coast,
valley, vale,
volcano,
ballplayer, baseball player,
groom, bridegroom,
scuba diver,
rapeseed,
daisy,
"yellow ladys slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",
corn,
acorn,
hip, rose hip, rosehip,
buckeye, horse chestnut, conker,
coral fungus,
agaric,
gyromitra,
stinkhorn, carrion fungus,
earthstar,
hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa,
bolete,
ear, spike, capitulum,
toilet tissue, toilet paper, bathroom tissue
python/examples/pipeline/PaddleClas/ShuffleNetV2_x1_0/pipeline_http_client.py 0 → 100644
浏览文件 @ e9ecf97c
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')
if __name__ == "__main__":
url = "http://127.0.0.1:18080/imagenet/prediction"
with open(os.path.join(".", "daisy.jpg"), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
data = {"key": ["image"], "value": [image]}
for i in range(100):
r = requests.post(url=url, data=json.dumps(data))
print(r.json())
python/examples/pipeline/PaddleClas/ShuffleNetV2_x1_0/pipeline_rpc_client.py 0 → 100644
浏览文件 @ e9ecf97c
# 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.pipeline import PipelineClient
except ImportError:
from paddle_serving_server.pipeline import PipelineClient
import numpy as np
import requests
import json
import cv2
import base64
import os
client = PipelineClient()
client.connect(['127.0.0.1:9993'])
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/PaddleClas/ShuffleNetV2_x1_0/resnet50_web_service.py 0 → 100644
浏览文件 @ e9ecf97c
# 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()
batch_size = len(input_dict.keys())
imgs = []
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)
img = self.seq(im)
imgs.append(img[np.newaxis, :].copy())
input_imgs = np.concatenate(imgs, axis=0)
return {"image": input_imgs}, False, None, ""
def postprocess(self, input_dicts, fetch_dict, log_id):
score_list = fetch_dict["prediction"]
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/pipeline/PaddleDetection/faster_rcnn/benchmark.py
浏览文件 @ e9ecf97c
...@@ -46,23 +46,43 @@ def run_http(idx, batch_size): ...@@ -46,23 +46,43 @@ def run_http(idx, batch_size):
with open(os.path.join(".", "000000570688.jpg"), 'rb') as file: with open(os.path.join(".", "000000570688.jpg"), 'rb') as file:
image_data1 = file.read() image_data1 = file.read()
image = cv2_to_base64(image_data1) image = cv2_to_base64(image_data1)
latency_list = []
start = time.time() start = time.time()
total_num = 0
while True: while True:
l_start = time.time()
data = {"key": [], "value": []} data = {"key": [], "value": []}
for j in range(batch_size): for j in range(batch_size):
data["key"].append("image_" + str(j)) data["key"].append("image_" + str(j))
data["value"].append(image) data["value"].append(image)
r = requests.post(url=url, data=json.dumps(data)) r = requests.post(url=url, data=json.dumps(data))
l_end = time.time()
total_num += 1
end = time.time() end = time.time()
latency_list.append(l_end * 1000 - l_start * 1000)
if end - start > 70: if end - start > 70:
print("70s end") #print("70s end")
break break
return [[end - start]] return [[end - start], latency_list, [total_num]]
def multithread_http(thread, batch_size): def multithread_http(thread, batch_size):
multi_thread_runner = MultiThreadRunner() multi_thread_runner = MultiThreadRunner()
result = multi_thread_runner.run(run_http , thread, batch_size) start = time.time()
result = multi_thread_runner.run(run_http, thread, batch_size)
end = time.time()
total_cost = end - start
avg_cost = 0
total_number = 0
for i in range(thread):
avg_cost += result[0][i]
total_number += result[2][i]
avg_cost = avg_cost / thread
print("Total cost: {}s".format(total_cost))
print("Each thread cost: {}s. ".format(avg_cost))
print("Total count: {}. ".format(total_number))
print("AVG QPS: {} samples/s".format(batch_size * total_number /
total_cost))
show_latency(result[1])
def run_rpc(thread, batch_size): def run_rpc(thread, batch_size):
pass pass
......
python/examples/pipeline/PaddleDetection/faster_rcnn/benchmark.sh
浏览文件 @ e9ecf97c
export FLAGS_profile_pipeline=1 export FLAGS_profile_pipeline=1
alias python3="python3.7" alias python3="python3.6"
modelname="faster_rcnn_r50_fpn_1x_coco" modelname="det-FasterRCNN"
gpu_id="0"
benchmark_config_filename="benchmark_config.yaml"
# HTTP # HTTP
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9 #ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3 sleep 3
python3 benchmark.py yaml local_predictor 1 gpu $gpu_id # Create yaml，If you already have the config.yaml, ignore it.
#python3 benchmark.py yaml local_predictor 1 gpu
rm -rf profile_log_$modelname rm -rf profile_log_$modelname
for thread_num in 1
echo "Starting HTTP Clients..."
# Start a client in each thread, tesing the case of multiple threads.
for thread_num in 1 2 4 8 12 16
do do
for batch_size in 1 for batch_size in 1
do do
echo "#----FasterRCNN thread num: $thread_num batch size: $batch_size mode:http ----" >>profile_log_$modelname echo "----${modelname} thread num: ${thread_num} batch size: ${batch_size} mode:http ----" >>profile_log_$modelname
rm -rf PipelineServingLogs # Start one web service, If you start the service yourself, you can ignore it here.
rm -rf cpu_utilization.py #python3 web_service.py >web.log 2>&1 &
python3 web_service.py >web.log 2>&1 & #sleep 3
sleep 3
nvidia-smi --id=${gpu_id} --query-compute-apps=used_memory --format=csv -lms 100 > gpu_use.log 2>&1 & # --id is the serial number of the GPU card, Must be the same as the gpu id used by the server.
nvidia-smi --id=${gpu_id} --query-gpu=utilization.gpu --format=csv -lms 100 > gpu_utilization.log 2>&1 & nvidia-smi --id=3 --query-gpu=memory.used --format=csv -lms 1000 > gpu_use.log 2>&1 &
nvidia-smi --id=3 --query-gpu=utilization.gpu --format=csv -lms 1000 > gpu_utilization.log 2>&1 &
echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py 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 $batch_size # Start http client
python3 cpu_utilization.py >>profile_log_$modelname python3 benchmark.py run http $thread_num $batch_size > profile 2>&1
python3 -m paddle_serving_server_gpu.profiler >>profile_log_$modelname
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9 # Collect CPU metrics, Filter data that is zero momentarily, Record the maximum value of GPU memory and the average value of GPU utilization
ps -ef | grep nvidia-smi | awk '{print $2}' | xargs kill -9 python3 cpu_utilization.py >> profile_log_$modelname
python3 benchmark.py dump benchmark.log benchmark.tmp grep -av '^0 %' gpu_utilization.log > gpu_utilization.log.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=$modelname}} END {print "GPU_MEM:", max}' gpu_use.log >> profile_log_$modelname awk -F' ' '{sum+=$1} END {print "GPU_UTILIZATION:", sum/NR, sum, NR }' gpu_utilization.log.tmp >> profile_log_$modelname
awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "GPU_UTIL:", max}' gpu_utilization.log >> profile_log_$modelname
cat benchmark.log >> profile_log_$modelname # Show profiles
python3 -m paddle_serving_server_gpu.parse_profile --benchmark_cfg $benchmark_config_filename --benchmark_log profile_log_$modelname python3 ../../../util/show_profile.py profile $thread_num >> profile_log_$modelname
#rm -rf gpu_use.log gpu_utilization.log tail -n 8 profile >> profile_log_$modelname
echo '' >> profile_log_$modelname
done done
done done
# Kill all nvidia-smi background task.
pkill nvidia-smi
python/examples/pipeline/PaddleDetection/faster_rcnn/web_service.py
浏览文件 @ e9ecf97c
...@@ -64,5 +64,5 @@ class FasterRCNNService(WebService): ...@@ -64,5 +64,5 @@ class FasterRCNNService(WebService):
fasterrcnn_service = FasterRCNNService(name="faster_rcnn") fasterrcnn_service = FasterRCNNService(name="faster_rcnn")
fasterrcnn_service.prepare_pipeline_config("config2.yml") fasterrcnn_service.prepare_pipeline_config("config.yml")
fasterrcnn_service.run_service() fasterrcnn_service.run_service()
python/examples/pipeline/PaddleDetection/ppyolo_mbv3/benchmark.py
浏览文件 @ e9ecf97c
...@@ -36,7 +36,7 @@ def gen_yml(device, gpu_id): ...@@ -36,7 +36,7 @@ def gen_yml(device, gpu_id):
config["dag"]["tracer"] = {"interval_s": 30} config["dag"]["tracer"] = {"interval_s": 30}
if device == "gpu": if device == "gpu":
config["op"]["ppyolo_mbv3"]["local_service_conf"]["device_type"] = 1 config["op"]["ppyolo_mbv3"]["local_service_conf"]["device_type"] = 1
config["op"]["ppyolo_mbv3"]["local_service_conf"]["devices"] = gpu_id config["op"]["ppyolo_mbv3"]["local_service_conf"]["devices"] = gpu_id
with open("config2.yml", "w") as fout: with open("config2.yml", "w") as fout:
yaml.dump(config, fout, default_flow_style=False) yaml.dump(config, fout, default_flow_style=False)
...@@ -46,23 +46,43 @@ def run_http(idx, batch_size): ...@@ -46,23 +46,43 @@ def run_http(idx, batch_size):
with open(os.path.join(".", "000000570688.jpg"), 'rb') as file: with open(os.path.join(".", "000000570688.jpg"), 'rb') as file:
image_data1 = file.read() image_data1 = file.read()
image = cv2_to_base64(image_data1) image = cv2_to_base64(image_data1)
latency_list = []
start = time.time() start = time.time()
total_num = 0
while True: while True:
l_start = time.time()
data = {"key": [], "value": []} data = {"key": [], "value": []}
for j in range(batch_size): for j in range(batch_size):
data["key"].append("image_" + str(j)) data["key"].append("image_" + str(j))
data["value"].append(image) data["value"].append(image)
r = requests.post(url=url, data=json.dumps(data)) r = requests.post(url=url, data=json.dumps(data))
l_end = time.time()
total_num += 1
end = time.time() end = time.time()
latency_list.append(l_end * 1000 - l_start * 1000)
if end - start > 70: if end - start > 70:
print("70s end") #print("70s end")
break break
return [[end - start]] return [[end - start], latency_list, [total_num]]
def multithread_http(thread, batch_size): def multithread_http(thread, batch_size):
multi_thread_runner = MultiThreadRunner() multi_thread_runner = MultiThreadRunner()
result = multi_thread_runner.run(run_http , thread, batch_size) start = time.time()
result = multi_thread_runner.run(run_http, thread, batch_size)
end = time.time()
total_cost = end - start
avg_cost = 0
total_number = 0
for i in range(thread):
avg_cost += result[0][i]
total_number += result[2][i]
avg_cost = avg_cost / thread
print("Total cost: {}s".format(total_cost))
print("Each thread cost: {}s. ".format(avg_cost))
print("Total count: {}. ".format(total_number))
print("AVG QPS: {} samples/s".format(batch_size * total_number /
total_cost))
show_latency(result[1])
def run_rpc(thread, batch_size): def run_rpc(thread, batch_size):
pass pass
......
python/examples/pipeline/PaddleDetection/ppyolo_mbv3/benchmark.sh
浏览文件 @ e9ecf97c
export FLAGS_profile_pipeline=1 export FLAGS_profile_pipeline=1
alias python3="python3.7" alias python3="python3.6"
modelname="ppyolo_mbv3_large" modelname="det-PPYoloMbv3"
gpu_id="0"
benchmark_config_filename="benchmark_config.yaml"
# HTTP # HTTP
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9 #ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3 sleep 3
python3 benchmark.py yaml local_predictor 1 gpu $gpu_id # Create yaml，If you already have the config.yaml, ignore it.
#python3 benchmark.py yaml local_predictor 1 gpu
rm -rf profile_log_$modelname rm -rf profile_log_$modelname
for thread_num in 1
echo "Starting HTTP Clients..."
# Start a client in each thread, tesing the case of multiple threads.
for thread_num in 1 2 4 8 12 16
do do
for batch_size in 1 for batch_size in 1
do do
echo "#----PPyolo thread num: $thread_num batch size: $batch_size mode:http ----" >>profile_log_$modelname echo "----${modelname} thread num: ${thread_num} batch size: ${batch_size} mode:http ----" >>profile_log_$modelname
rm -rf PipelineServingLogs # Start one web service, If you start the service yourself, you can ignore it here.
rm -rf cpu_utilization.py #python3 web_service.py >web.log 2>&1 &
python3 web_service.py >web.log 2>&1 & #sleep 3
sleep 3
nvidia-smi --id=${gpu_id} --query-compute-apps=used_memory --format=csv -lms 100 > gpu_use.log 2>&1 & # --id is the serial number of the GPU card, Must be the same as the gpu id used by the server.
nvidia-smi --id=${gpu_id} --query-gpu=utilization.gpu --format=csv -lms 100 > gpu_utilization.log 2>&1 & nvidia-smi --id=3 --query-gpu=memory.used --format=csv -lms 1000 > gpu_use.log 2>&1 &
nvidia-smi --id=3 --query-gpu=utilization.gpu --format=csv -lms 1000 > gpu_utilization.log 2>&1 &
echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py 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 $batch_size # Start http client
python3 cpu_utilization.py >>profile_log_$modelname python3 benchmark.py run http $thread_num $batch_size > profile 2>&1
python3 -m paddle_serving_server_gpu.profiler >>profile_log_$modelname
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9 # Collect CPU metrics, Filter data that is zero momentarily, Record the maximum value of GPU memory and the average value of GPU utilization
python3 benchmark.py dump benchmark.log benchmark.tmp python3 cpu_utilization.py >> profile_log_$modelname
mv benchmark.tmp benchmark.log grep -av '^0 %' gpu_utilization.log > gpu_utilization.log.tmp
awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "GPU_MEM:", max}' gpu_use.log >> profile_log_$modelname awk 'BEGIN {max = 0} {if(NR>1){if ($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_UTIL:", max}' gpu_utilization.log >> profile_log_$modelname awk -F' ' '{sum+=$1} END {print "GPU_UTILIZATION:", sum/NR, sum, NR }' gpu_utilization.log.tmp >> profile_log_$modelname
cat benchmark.log >> profile_log_$modelname
python3 -m paddle_serving_server_gpu.parse_profile --benchmark_cfg $benchmark_config_filename --benchmark_log profile_log_$modelname # Show profiles
#rm -rf gpu_use.log gpu_utilization.log python3 ../../../util/show_profile.py profile $thread_num >> profile_log_$modelname
tail -n 8 profile >> profile_log_$modelname
echo '' >> profile_log_$modelname
done done
done done
# Kill all nvidia-smi background task.
pkill nvidia-smi
python/examples/pipeline/PaddleDetection/ppyolo_mbv3/web_service.py
浏览文件 @ e9ecf97c
...@@ -65,5 +65,5 @@ class PPYoloMbv(WebService): ...@@ -65,5 +65,5 @@ class PPYoloMbv(WebService):
ppyolo_mbv3_service = PPYoloMbv(name="ppyolo_mbv3") ppyolo_mbv3_service = PPYoloMbv(name="ppyolo_mbv3")
ppyolo_mbv3_service.prepare_pipeline_config("config2.yml") ppyolo_mbv3_service.prepare_pipeline_config("config.yml")
ppyolo_mbv3_service.run_service() ppyolo_mbv3_service.run_service()
python/examples/pipeline/PaddleDetection/yolov3/benchmark.py
浏览文件 @ e9ecf97c
...@@ -35,8 +35,8 @@ def gen_yml(device, gpu_id): ...@@ -35,8 +35,8 @@ def gen_yml(device, gpu_id):
fin.close() fin.close()
config["dag"]["tracer"] = {"interval_s": 30} config["dag"]["tracer"] = {"interval_s": 30}
if device == "gpu": if device == "gpu":
config["op"]["faster_rcnn"]["local_service_conf"]["device_type"] = 1 config["op"]["yolov3"]["local_service_conf"]["device_type"] = 1
config["op"]["faster_rcnn"]["local_service_conf"]["devices"] = gpu_id config["op"]["yolov3"]["local_service_conf"]["devices"] = gpu_id
with open("config2.yml", "w") as fout: with open("config2.yml", "w") as fout:
yaml.dump(config, fout, default_flow_style=False) yaml.dump(config, fout, default_flow_style=False)
...@@ -46,23 +46,43 @@ def run_http(idx, batch_size): ...@@ -46,23 +46,43 @@ def run_http(idx, batch_size):
with open(os.path.join(".", "000000570688.jpg"), 'rb') as file: with open(os.path.join(".", "000000570688.jpg"), 'rb') as file:
image_data1 = file.read() image_data1 = file.read()
image = cv2_to_base64(image_data1) image = cv2_to_base64(image_data1)
latency_list = []
start = time.time() start = time.time()
total_num = 0
while True: while True:
l_start = time.time()
data = {"key": [], "value": []} data = {"key": [], "value": []}
for j in range(batch_size): for j in range(batch_size):
data["key"].append("image_" + str(j)) data["key"].append("image_" + str(j))
data["value"].append(image) data["value"].append(image)
r = requests.post(url=url, data=json.dumps(data)) r = requests.post(url=url, data=json.dumps(data))
l_end = time.time()
total_num += 1
end = time.time() end = time.time()
latency_list.append(l_end * 1000 - l_start * 1000)
if end - start > 70: if end - start > 70:
print("70s end") #print("70s end")
break break
return [[end - start]] return [[end - start], latency_list, [total_num]]
def multithread_http(thread, batch_size): def multithread_http(thread, batch_size):
multi_thread_runner = MultiThreadRunner() multi_thread_runner = MultiThreadRunner()
result = multi_thread_runner.run(run_http , thread, batch_size) start = time.time()
result = multi_thread_runner.run(run_http, thread, batch_size)
end = time.time()
total_cost = end - start
avg_cost = 0
total_number = 0
for i in range(thread):
avg_cost += result[0][i]
total_number += result[2][i]
avg_cost = avg_cost / thread
print("Total cost: {}s".format(total_cost))
print("Each thread cost: {}s. ".format(avg_cost))
print("Total count: {}. ".format(total_number))
print("AVG QPS: {} samples/s".format(batch_size * total_number /
total_cost))
show_latency(result[1])
def run_rpc(thread, batch_size): def run_rpc(thread, batch_size):
pass pass
......
python/examples/pipeline/PaddleDetection/yolov3/benchmark.sh
浏览文件 @ e9ecf97c
export FLAGS_profile_pipeline=1 export FLAGS_profile_pipeline=1
alias python3="python3.7" alias python3="python3.6"
modelname="yolov3_darknet53_270e_coco" modelname="det-yolov3"
gpu_id="0"
benchmark_config_filename="benchmark_config.yaml"
# HTTP # HTTP
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9 #ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3 sleep 3
python3 benchmark.py yaml local_predictor 1 cpu # Create yaml，If you already have the config.yaml, ignore it.
#python3 benchmark.py yaml local_predictor 1 gpu
rm -rf profile_log_$modelname rm -rf profile_log_$modelname
for thread_num in 1 8 16
echo "Starting HTTP Clients..."
# Start a client in each thread, tesing the case of multiple threads.
for thread_num in 1 2 4 8 12 16
do do
for batch_size in 1 for batch_size in 1
do do
echo "#----Yolov3 thread num: $thread_num batch size: $batch_size mode:http ----" >>profile_log_$modelname echo "----${modelname} thread num: ${thread_num} batch size: ${batch_size} mode:http ----" >>profile_log_$modelname
rm -rf PipelineServingLogs # Start one web service, If you start the service yourself, you can ignore it here.
rm -rf cpu_utilization.py #python3 web_service.py >web.log 2>&1 &
python3 web_service.py >web.log 2>&1 & #sleep 3
sleep 3
nvidia-smi --id=${gpu_id} --query-compute-apps=used_memory --format=csv -lms 100 > gpu_use.log 2>&1 & # --id is the serial number of the GPU card, Must be the same as the gpu id used by the server.
nvidia-smi --id=${gpu_id} --query-gpu=utilization.gpu --format=csv -lms 100 > gpu_utilization.log 2>&1 & nvidia-smi --id=3 --query-gpu=memory.used --format=csv -lms 1000 > gpu_use.log 2>&1 &
nvidia-smi --id=3 --query-gpu=utilization.gpu --format=csv -lms 1000 > gpu_utilization.log 2>&1 &
echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py 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 $batch_size # Start http client
python3 cpu_utilization.py >>profile_log_$modelname python3 benchmark.py run http $thread_num $batch_size > profile 2>&1
python3 -m paddle_serving_server_gpu.profiler >>profile_log_$modelname
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9 # Collect CPU metrics, Filter data that is zero momentarily, Record the maximum value of GPU memory and the average value of GPU utilization
python3 benchmark.py dump benchmark.log benchmark.tmp python3 cpu_utilization.py >> profile_log_$modelname
mv benchmark.tmp benchmark.log grep -av '^0 %' gpu_utilization.log > gpu_utilization.log.tmp
awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "GPU_MEM:", max}' gpu_use.log >> profile_log_$modelname awk 'BEGIN {max = 0} {if(NR>1){if ($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_UTIL:", max}' gpu_utilization.log >> profile_log_$modelname awk -F' ' '{sum+=$1} END {print "GPU_UTILIZATION:", sum/NR, sum, NR }' gpu_utilization.log.tmp >> profile_log_$modelname
cat benchmark.log >> profile_log_$modelname
python3 -m paddle_serving_server_gpu.parse_profile --benchmark_cfg $benchmark_config_filename --benchmark_log profile_log_$modelname # Show profiles
#rm -rf gpu_use.log gpu_utilization.log python3 ../../../util/show_profile.py profile $thread_num >> profile_log_$modelname
tail -n 8 profile >> profile_log_$modelname
echo '' >> profile_log_$modelname
done done
done done
# Kill all nvidia-smi background task.
pkill nvidia-smi
python/examples/pipeline/PaddleDetection/yolov3/web_service.py
浏览文件 @ e9ecf97c
...@@ -64,5 +64,5 @@ class Yolov3Service(WebService): ...@@ -64,5 +64,5 @@ class Yolov3Service(WebService):
yolov3_service = Yolov3Service(name="yolov3") yolov3_service = Yolov3Service(name="yolov3")
yolov3_service.prepare_pipeline_config("config2.yml") yolov3_service.prepare_pipeline_config("config.yml")
yolov3_service.run_service() yolov3_service.run_service()
tools/auth/auth-serving-docker.yaml 0 → 100644
浏览文件 @ e9ecf97c
version: '3'
volumes:
kong_data: {}
services:
db:
image: postgres:9.6
environment:
- POSTGRES_DB=kong
- POSTGRES_USER=kong
- POSTGRES_PASSWORD=kong
volumes:
- kong_data:/var/lib/postgresql/data
healthcheck:
test: ["CMD-SHELL", "pg_isready -U postgres"]
interval: 10s
timeout: 5s
retries: 5
restart: always
kong-migrations:
image: registry.baidubce.com/serving_gateway/kong:paddle
command: kong migrations bootstrap
depends_on:
- db
environment:
- KONG_DATABASE=postgres
- KONG_PG_DATABASE=kong
- KONG_PG_HOST=db
- KONG_PG_USER=kong
- KONG_PG_PASSWORD=kong
restart: on-failure
kong:
image: registry.baidubce.com/serving_gateway/kong:paddle
depends_on:
- db
- redis
environment:
- KONG_DATABASE=postgres
- KONG_PG_HOST=db
- KONG_PG_DATABASE=kong
- KONG_PG_USER=kong
- KONG_PG_PASSWORD=kong
# - KONGKA_REDIS_HOST=redis
# - KONGKA_REDIS_PORT=6379
# - KONGKA_REDIS_DATABASE=0
ports:
- 8000:8000/tcp
- 127.0.0.1:8001:8001/tcp
- 8443:8443/tcp
- 127.0.0.1:8444:8444/tcp
healthcheck:
test: ["CMD", "kong", "health"]
interval: 10s
timeout: 10s
retries: 10
restart: always
kong-prepare:
image: registry.baidubce.com/serving_gateway/kong:paddle
entrypoint: ["bash", "/autoconfigure-admin-api.sh"]
depends_on:
- kong
restart: on-failure
konga-prepare:
image: pantsel/konga:next
command: -c prepare -a postgres -u postgresql://kong:kong@db/konga
depends_on:
- db
restart: on-failure
healthcheck:
test: "exit 0"
konga:
image: pantsel/konga:next
environment:
- DB_ADAPTER=postgres
- DB_HOST=db
- DB_USER=kong
- DB_PASSWORD=kong
- DB_DATABASE=konga
- NODE_ENV=production
depends_on:
- db
- konga-prepare
ports:
- 8005:1337/tcp
restart: always
redis:
image: redis:latest
ports:
- 6379:6379
restart: always
serving:
image: registry.baidubce.com/serving_dev/serving-runtime:cpu-py36
ports:
- 9393:9393
command: bash -c "
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/uci_housing.tar.gz
&& tar -xzf uci_housing.tar.gz
&& python3.6 -m paddle_serving_server.serve --model uci_housing_model --thread 10 --port 9393 --name uci
"
restart: always
tools/auth/key-auth-k8s.yaml 0 → 100644
浏览文件 @ e9ecf97c
apiVersion: configuration.konghq.com/v1
kind: KongPlugin
metadata:
name: key-auth
plugin: key-auth
tools/auth/kong-consumer-k8s.yaml 0 → 100644
浏览文件 @ e9ecf97c
apiVersion: configuration.konghq.com/v1
kind: KongConsumer
metadata:
name: default
annotations:
kubernetes.io/ingress.class: kong
username: default
credentials:
- default-apikey
tools/auth/kong-ingress-k8s.yaml 0 → 100644
浏览文件 @ e9ecf97c
apiVersion: extensions/v1beta1
kind: Ingress
metadata:
name: demo
annotations:
konghq.com/strip-path: "true"
kubernetes.io/ingress.class: kong
spec:
rules:
- http:
paths:
- path: /foo
backend:
serviceName: uci
servicePort: 9393
tools/auth/kong-ingress.yaml 0 → 100644
浏览文件 @ e9ecf97c
apiVersion: v1
kind: Namespace
metadata:
name: kong
---
apiVersion: apiextensions.k8s.io/v1beta1
kind: CustomResourceDefinition
metadata:
name: kongclusterplugins.configuration.konghq.com
spec:
additionalPrinterColumns:
- JSONPath: .plugin
description: Name of the plugin
name: Plugin-Type
type: string
- JSONPath: .metadata.creationTimestamp
description: Age
name: Age
type: date
- JSONPath: .disabled
description: Indicates if the plugin is disabled
name: Disabled
priority: 1
type: boolean
- JSONPath: .config
description: Configuration of the plugin
name: Config
priority: 1
type: string
group: configuration.konghq.com
names:
kind: KongClusterPlugin
plural: kongclusterplugins
shortNames:
- kcp
scope: Cluster
subresources:
status: {}
validation:
openAPIV3Schema:
properties:
config:
type: object
configFrom:
properties:
secretKeyRef:
properties:
key:
type: string
name:
type: string
namespace:
type: string
required:
- name
- namespace
- key
type: object
type: object
disabled:
type: boolean
plugin:
type: string
protocols:
items:
enum:
- http
- https
- grpc
- grpcs
- tcp
- tls
type: string
type: array
run_on:
enum:
- first
- second
- all
type: string
required:
- plugin
version: v1
---
apiVersion: apiextensions.k8s.io/v1beta1
kind: CustomResourceDefinition
metadata:
name: kongconsumers.configuration.konghq.com
spec:
additionalPrinterColumns:
- JSONPath: .username
description: Username of a Kong Consumer
name: Username
type: string
- JSONPath: .metadata.creationTimestamp
description: Age
name: Age
type: date
group: configuration.konghq.com
names:
kind: KongConsumer
plural: kongconsumers
shortNames:
- kc
scope: Namespaced
subresources:
status: {}
validation:
openAPIV3Schema:
properties:
credentials:
items:
type: string
type: array
custom_id:
type: string
username:
type: string
version: v1
---
apiVersion: apiextensions.k8s.io/v1beta1
kind: CustomResourceDefinition
metadata:
name: kongingresses.configuration.konghq.com
spec:
group: configuration.konghq.com
names:
kind: KongIngress
plural: kongingresses
shortNames:
- ki
scope: Namespaced
subresources:
status: {}
validation:
openAPIV3Schema:
properties:
proxy:
properties:
connect_timeout:
minimum: 0
type: integer
path:
pattern: ^/.*$
type: string
protocol:
enum:
- http
- https
- grpc
- grpcs
- tcp
- tls
type: string
read_timeout:
minimum: 0
type: integer
retries:
minimum: 0
type: integer
write_timeout:
minimum: 0
type: integer
type: object
route:
properties:
headers:
additionalProperties:
items:
type: string
type: array
type: object
https_redirect_status_code:
type: integer
methods:
items:
type: string
type: array
path_handling:
enum:
- v0
- v1
type: string
preserve_host:
type: boolean
protocols:
items:
enum:
- http
- https
- grpc
- grpcs
- tcp
- tls
type: string
type: array
regex_priority:
type: integer
request_buffering:
type: boolean
response_buffering:
type: boolean
snis:
items:
type: string
type: array
strip_path:
type: boolean
upstream:
properties:
algorithm:
enum:
- round-robin
- consistent-hashing
- least-connections
type: string
hash_fallback:
type: string
hash_fallback_header:
type: string
hash_on:
type: string
hash_on_cookie:
type: string
hash_on_cookie_path:
type: string
hash_on_header:
type: string
healthchecks:
properties:
active:
properties:
concurrency:
minimum: 1
type: integer
healthy:
properties:
http_statuses:
items:
type: integer
type: array
interval:
minimum: 0
type: integer
successes:
minimum: 0
type: integer
type: object
http_path:
pattern: ^/.*$
type: string
timeout:
minimum: 0
type: integer
unhealthy:
properties:
http_failures:
minimum: 0
type: integer
http_statuses:
items:
type: integer
type: array
interval:
minimum: 0
type: integer
tcp_failures:
minimum: 0
type: integer
timeout:
minimum: 0
type: integer
type: object
type: object
passive:
properties:
healthy:
properties:
http_statuses:
items:
type: integer
type: array
interval:
minimum: 0
type: integer
successes:
minimum: 0
type: integer
type: object
unhealthy:
properties:
http_failures:
minimum: 0
type: integer
http_statuses:
items:
type: integer
type: array
interval:
minimum: 0
type: integer
tcp_failures:
minimum: 0
type: integer
timeout:
minimum: 0
type: integer
type: object
type: object
threshold:
type: integer
type: object
host_header:
type: string
slots:
minimum: 10
type: integer
type: object
version: v1
---
apiVersion: apiextensions.k8s.io/v1beta1
kind: CustomResourceDefinition
metadata:
name: kongplugins.configuration.konghq.com
spec:
additionalPrinterColumns:
- JSONPath: .plugin
description: Name of the plugin
name: Plugin-Type
type: string
- JSONPath: .metadata.creationTimestamp
description: Age
name: Age
type: date
- JSONPath: .disabled
description: Indicates if the plugin is disabled
name: Disabled
priority: 1
type: boolean
- JSONPath: .config
description: Configuration of the plugin
name: Config
priority: 1
type: string
group: configuration.konghq.com
names:
kind: KongPlugin
plural: kongplugins
shortNames:
- kp
scope: Namespaced
subresources:
status: {}
validation:
openAPIV3Schema:
properties:
config:
type: object
configFrom:
properties:
secretKeyRef:
properties:
key:
type: string
name:
type: string
required:
- name
- key
type: object
type: object
disabled:
type: boolean
plugin:
type: string
protocols:
items:
enum:
- http
- https
- grpc
- grpcs
- tcp
- tls
type: string
type: array
run_on:
enum:
- first
- second
- all
type: string
required:
- plugin
version: v1
---
apiVersion: apiextensions.k8s.io/v1beta1
kind: CustomResourceDefinition
metadata:
name: tcpingresses.configuration.konghq.com
spec:
additionalPrinterColumns:
- JSONPath: .status.loadBalancer.ingress[*].ip
description: Address of the load balancer
name: Address
type: string
- JSONPath: .metadata.creationTimestamp
description: Age
name: Age
type: date
group: configuration.konghq.com
names:
kind: TCPIngress
plural: tcpingresses
scope: Namespaced
subresources:
status: {}
validation:
openAPIV3Schema:
properties:
apiVersion:
type: string
kind:
type: string
metadata:
type: object
spec:
properties:
rules:
items:
properties:
backend:
properties:
serviceName:
type: string
servicePort:
format: int32
type: integer
type: object
host:
type: string
port:
format: int32
type: integer
type: object
type: array
tls:
items:
properties:
hosts:
items:
type: string
type: array
secretName:
type: string
type: object
type: array
type: object
status:
type: object
version: v1beta1
status:
acceptedNames:
kind: ""
plural: ""
conditions: []
storedVersions: []
---
apiVersion: v1
kind: ServiceAccount
metadata:
name: kong-serviceaccount
namespace: kong
---
apiVersion: rbac.authorization.k8s.io/v1beta1
kind: ClusterRole
metadata:
name: kong-ingress-clusterrole
rules:
- apiGroups:
- ""
resources:
- endpoints
- nodes
- pods
- secrets
verbs:
- list
- watch
- apiGroups:
- ""
resources:
- nodes
verbs:
- get
- apiGroups:
- ""
resources:
- services
verbs:
- get
- list
- watch
- apiGroups:
- networking.k8s.io
- extensions
- networking.internal.knative.dev
resources:
- ingresses
verbs:
- get
- list
- watch
- apiGroups:
- ""
resources:
- events
verbs:
- create
- patch
- apiGroups:
- networking.k8s.io
- extensions
- networking.internal.knative.dev
resources:
- ingresses/status
verbs:
- update
- apiGroups:
- configuration.konghq.com
resources:
- tcpingresses/status
verbs:
- update
- apiGroups:
- configuration.konghq.com
resources:
- kongplugins
- kongclusterplugins
- kongcredentials
- kongconsumers
- kongingresses
- tcpingresses
verbs:
- get
- list
- watch
- apiGroups:
- ""
resources:
- configmaps
verbs:
- create
- get
- update
---
apiVersion: rbac.authorization.k8s.io/v1beta1
kind: ClusterRoleBinding
metadata:
name: kong-ingress-clusterrole-nisa-binding
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: ClusterRole
name: kong-ingress-clusterrole
subjects:
- kind: ServiceAccount
name: kong-serviceaccount
namespace: kong
---
apiVersion: v1
kind: Service
metadata:
annotations:
service.beta.kubernetes.io/aws-load-balancer-backend-protocol: tcp
service.beta.kubernetes.io/aws-load-balancer-type: nlb
name: kong-proxy
namespace: kong
spec:
ports:
- name: proxy
port: 80
protocol: TCP
targetPort: 8000
- name: proxy-ssl
port: 443
protocol: TCP
targetPort: 8443
selector:
app: ingress-kong
type: NodePort
---
apiVersion: v1
kind: Service
metadata:
name: kong-validation-webhook
namespace: kong
spec:
ports:
- name: webhook
port: 443
protocol: TCP
targetPort: 8080
selector:
app: ingress-kong
---
apiVersion: apps/v1
kind: Deployment
metadata:
labels:
app: ingress-kong
name: ingress-kong
namespace: kong
spec:
replicas: 1
selector:
matchLabels:
app: ingress-kong
template:
metadata:
annotations:
kuma.io/gateway: enabled
prometheus.io/port: "8100"
prometheus.io/scrape: "true"
traffic.sidecar.istio.io/includeInboundPorts: ""
labels:
app: ingress-kong
spec:
containers:
- env:
- name: KONG_PROXY_LISTEN
value: 0.0.0.0:8000, 0.0.0.0:8443 ssl http2
- name: KONG_PORT_MAPS
value: 80:8000, 443:8443
- name: KONG_ADMIN_LISTEN
value: 127.0.0.1:8444 ssl
- name: KONG_STATUS_LISTEN
value: 0.0.0.0:8100
- name: KONG_DATABASE
value: "off"
- name: KONG_NGINX_WORKER_PROCESSES
value: "2"
- name: KONG_ADMIN_ACCESS_LOG
value: /dev/stdout
- name: KONG_ADMIN_ERROR_LOG
value: /dev/stderr
- name: KONG_PROXY_ERROR_LOG
value: /dev/stderr
image: registry.baidubce.com/serving_gateway/kong:paddle
lifecycle:
preStop:
exec:
command:
- /bin/sh
- -c
- kong quit
livenessProbe:
failureThreshold: 3
httpGet:
path: /status
port: 8100
scheme: HTTP
initialDelaySeconds: 5
periodSeconds: 10
successThreshold: 1
timeoutSeconds: 1
name: proxy
ports:
- containerPort: 8000
name: proxy
protocol: TCP
- containerPort: 8443
name: proxy-ssl
protocol: TCP
- containerPort: 8100
name: metrics
protocol: TCP
readinessProbe:
failureThreshold: 3
httpGet:
path: /status
port: 8100
scheme: HTTP
initialDelaySeconds: 5
periodSeconds: 10
successThreshold: 1
timeoutSeconds: 1
- env:
- name: CONTROLLER_KONG_ADMIN_URL
value: https://127.0.0.1:8444
- name: CONTROLLER_KONG_ADMIN_TLS_SKIP_VERIFY
value: "true"
- name: CONTROLLER_PUBLISH_SERVICE
value: kong/kong-proxy
- name: POD_NAME
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: metadata.name
- name: POD_NAMESPACE
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: metadata.namespace
image: kong/kubernetes-ingress-controller:1.2
imagePullPolicy: IfNotPresent
livenessProbe:
failureThreshold: 3
httpGet:
path: /healthz
port: 10254
scheme: HTTP
initialDelaySeconds: 5
periodSeconds: 10
successThreshold: 1
timeoutSeconds: 1
name: ingress-controller
ports:
- containerPort: 8080
name: webhook
protocol: TCP
readinessProbe:
failureThreshold: 3
httpGet:
path: /healthz
port: 10254
scheme: HTTP
initialDelaySeconds: 5
periodSeconds: 10
successThreshold: 1
timeoutSeconds: 1
serviceAccountName: kong-serviceaccount
tools/auth/serving-demo-k8s.yaml 0 → 100644
浏览文件 @ e9ecf97c
apiVersion: v1
kind: Service
metadata:
labels:
app: uci
name: uci
spec:
ports:
- port: 9393
name: http
protocol: TCP
targetPort: 9393
selector:
app: uci
---
apiVersion: apps/v1
kind: Deployment
metadata:
labels:
app: uci
name: uci
spec:
replicas: 1
selector:
matchLabels:
app: uci
strategy: {}
template:
metadata:
creationTimestamp: null
labels:
app: uci
spec:
containers:
- image: registry.baidubce.com/serving_dev/fit_a_line:security
name: uci
imagePullPolicy: Always
ports:
- containerPort: 9393
workingDir: /home/fit_a_line/
name: uci
command: ['/bin/bash', '-c']
args: ["python3.6 -m paddle_serving_server.serve --model uci_housing_model --thread 10 --port 9393 --name uci"]
env:
- name: SERVING_BIN
value: "/usr/local/serving_bin/serving"
- name: NODE_NAME
valueFrom:
fieldRef:
fieldPath: spec.nodeName
- name: POD_NAME
valueFrom:
fieldRef:
fieldPath: metadata.name
- name: POD_NAMESPACE
valueFrom:
fieldRef:
fieldPath: metadata.namespace
- name: POD_IP
valueFrom:
fieldRef:
fieldPath: status.podIP
resources: {}
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