提交 dbfcb515 编写于 作者: W wangjiawei04

add paddle detection and fix dependency

上级 d6691d1a
# Serve models from Paddle Detection
(English|[简体中文](./README_CN.md))
### Introduction
PaddleDetection flying paddle target detection development kit is designed to help developers complete the whole development process of detection model formation, training, optimization and deployment faster and better. For details, see [Github](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph)
This article mainly introduces the deployment of Paddle Detection's dynamic graph model on Serving.
Paddle Detection provides a large number of [Model Zoo](https://github.com/PaddlePaddle/PaddleDetection/blob/master/dygraph/docs/MODEL_ZOO_cn.md), these model libraries can be used in Paddle Serving with export tools Model. For the export tutorial, please refer to [Paddle Detection Export Model Tutorial (Simplified Chinese)](https://github.com/PaddlePaddle/PaddleDetection/blob/master/dygraph/deploy/EXPORT_MODEL.md).
### Serving example
Several examples of PaddleDetection models used in Serving are given in this folder
-[Faster RCNN](./faster_rcnn_r50_fpn_1x_coco)
-[PPYOLO](./ppyolo_r50vd_dcn_1x_coco)
-[TTFNet](./ttfnet_darknet53_1x_coco)
-[YOLOv3](./yolov3_darknet53_270e_coco)
## 使用Paddle Detection模型
([English](./README.md)|简体中文)
### 简介
PaddleDetection飞桨目标检测开发套件,旨在帮助开发者更快更好地完成检测模型的组建、训练、优化及部署等全开发流程。详情参见[Github](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph)
本文主要是介绍Paddle Detection的动态图模型在Serving上的部署。
### 导出模型
Paddle Detection提供了大量的[模型库](https://github.com/PaddlePaddle/PaddleDetection/blob/master/dygraph/docs/MODEL_ZOO_cn.md), 这些模型库配合导出工具都可以得到可以用于Paddle Serving的模型。导出教程参见[Paddle Detection模型导出教程](https://github.com/PaddlePaddle/PaddleDetection/blob/master/dygraph/deploy/EXPORT_MODEL.md)
### Serving示例
本文件夹下给出了多个PaddleDetection模型用于Serving的范例
- [Faster RCNN](./faster_rcnn_r50_fpn_1x_coco)
- [PPYOLO](./ppyolo_r50vd_dcn_1x_coco)
- [TTFNet](./ttfnet_darknet53_1x_coco)
- [YOLOv3](./yolov3_darknet53_270e_coco)
# Faster RCNN model on Paddle Serving
([简体中文](./README_CN.md)|English)
### Get The Faster RCNN Model
```
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/2.0/faster_rcnn_r50_fpn_1x_coco.tar
```
### Start the service
```
tar xf faster_rcnn_r50_fpn_1x_coco.tar
python -m paddle_serving_server_gpu.serve --model serving_server --port 9494 --gpu_ids 0
```
### Perform prediction
```
python test_client.py 000000570688.jpg
```
## 3. Result analysis
<p align = "center">
    <br>
<img src = '000000570688.jpg'>
    <br>
<p>
This is the input picture
  
<p align = "center">
    <br>
<img src = '000000570688_bbox.jpg'>
    <br>
<p>
This is the picture after adding bbox. You can see that the client has done post-processing for the picture. In addition, the output/bbox.json also has the number and coordinate information of each box.
# 使用Paddle Serving部署Faster RCNN模型
(简体中文|[English](./README.md))
## 获得Faster RCNN模型
```
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/2.0/faster_rcnn_r50_fpn_1x_coco.tar
```
### 启动服务
```
tar xf faster_rcnn_r50_fpn_1x_coco.tar
python -m paddle_serving_server_gpu.serve --model pddet_serving_model --port 9494 --gpu_ids 0
```
### 执行预测
```
python test_client.py 000000570688.jpg
```
## 3. 结果分析
<p align="center">
<br>
<img src='000000570688.jpg' >
<br>
<p>
这是输入图片
<p align="center">
<br>
<img src='000000570688_bbox.jpg' >
<br>
<p>
这是实现添加了bbox之后的图片,可以看到客户端已经为图片做好了后处理,此外在output/bbox.json也有各个框的编号和坐标信息。
person
bicycle
car
motorcycle
airplane
bus
train
truck
boat
traffic light
fire hydrant
stop sign
parking meter
bench
bird
cat
dog
horse
sheep
cow
elephant
bear
zebra
giraffe
backpack
umbrella
handbag
tie
suitcase
frisbee
skis
snowboard
sports ball
kite
baseball bat
baseball glove
skateboard
surfboard
tennis racket
bottle
wine glass
cup
fork
knife
spoon
bowl
banana
apple
sandwich
orange
broccoli
carrot
hot dog
pizza
donut
cake
chair
couch
potted plant
bed
dining table
toilet
tv
laptop
mouse
remote
keyboard
cell phone
microwave
oven
toaster
sink
refrigerator
book
clock
vase
scissors
teddy bear
hair drier
toothbrush
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle_serving_client import Client
from paddle_serving_app.reader import *
import sys
import numpy as np
preprocess = Sequential([
File2Image(), BGR2RGB(), Div(255.0),
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], False),
Resize(640, 640), Transpose((2, 0, 1))
])
postprocess = RCNNPostprocess("label_list.txt", "output")
client = Client()
client.load_client_config("serving_client/serving_client_conf.prototxt")
client.connect(['127.0.0.1:9494'])
im = preprocess(sys.argv[1])
fetch_map = client.predict(
feed={
"image": im,
"im_shape": np.array(list(im.shape[1:])).reshape(-1),
"scale_factor": np.array([1.0, 1.0]).reshape(-1),
},
fetch=["save_infer_model/scale_0.tmp_1"],
batch=False)
print(fetch_map)
fetch_map["image"] = sys.argv[1]
postprocess(fetch_map)
# PP-YOLO model on Paddle Serving
([简体中文](./README_CN.md)|English)
### Get The Model
```
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/2.0/ppyolo_r50vd_dcn_1x_coco.tar
```
### Start the service
```
tar xf ppyolo_r50vd_dcn_1x_coco.tar
python -m paddle_serving_server_gpu.serve --model serving_server --port 9494 --gpu_ids 0
```
### Perform prediction
```
python test_client.py 000000570688.jpg
```
# 使用Paddle Serving部署PP-YOLO模型
(简体中文|[English](./README.md))
## 获得模型
```
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/2.0/ppyolo_r50vd_dcn_1x_coco.tar
```
### 启动服务
```
tar xf ppyolo_r50vd_dcn_1x_coco.tar
python -m paddle_serving_server_gpu.serve --model pddet_serving_model --port 9494 --gpu_ids 0
```
### 执行预测
```
python test_client.py 000000570688.jpg
```
person
bicycle
car
motorcycle
airplane
bus
train
truck
boat
traffic light
fire hydrant
stop sign
parking meter
bench
bird
cat
dog
horse
sheep
cow
elephant
bear
zebra
giraffe
backpack
umbrella
handbag
tie
suitcase
frisbee
skis
snowboard
sports ball
kite
baseball bat
baseball glove
skateboard
surfboard
tennis racket
bottle
wine glass
cup
fork
knife
spoon
bowl
banana
apple
sandwich
orange
broccoli
carrot
hot dog
pizza
donut
cake
chair
couch
potted plant
bed
dining table
toilet
tv
laptop
mouse
remote
keyboard
cell phone
microwave
oven
toaster
sink
refrigerator
book
clock
vase
scissors
teddy bear
hair drier
toothbrush
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle_serving_client import Client
from paddle_serving_app.reader import *
import sys
import numpy as np
preprocess = Sequential([
File2Image(), BGR2RGB(), Div(255.0),
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], False),
Resize((608, 608)), Transpose((2, 0, 1))
])
postprocess = RCNNPostprocess("label_list.txt", "output")
client = Client()
client.load_client_config("serving_client/serving_client_conf.prototxt")
client.connect(['127.0.0.1:9494'])
im = preprocess(sys.argv[1])
fetch_map = client.predict(
feed={
"image": im,
"im_shape": np.array(list(im.shape[1:])).reshape(-1),
"scale_factor": np.array([1.0, 1.0]).reshape(-1),
},
fetch=["save_infer_model/scale_0.tmp_1"],
batch=False)
print(fetch_map)
fetch_map["image"] = sys.argv[1]
postprocess(fetch_map)
# TTF-Net model on Paddle Serving
([简体中文](./README_CN.md)|English)
### Get Model
```
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/2.0/ttfnet_darknet53_1x_coco.tar
```
### Start the service
```
tar xf ttfnet_darknet53_1x_coco.tar
python -m paddle_serving_server_gpu.serve --model serving_server --port 9494 --gpu_ids 0
```
### Perform prediction
```
python test_client.py 000000570688.jpg
```
# 使用Paddle Serving部署TTF-Net模型
(简体中文|[English](./README.md))
## 获得模型
```
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/2.0/ttfnet_darknet53_1x_coco.tar
```
### 启动服务
```
tar xf ttfnet_darknet53_1x_coco.tar
python -m paddle_serving_server_gpu.serve --model pddet_serving_model --port 9494 --gpu_ids 0
```
### 执行预测
```
python test_client.py 000000570688.jpg
```
person
bicycle
car
motorcycle
airplane
bus
train
truck
boat
traffic light
fire hydrant
stop sign
parking meter
bench
bird
cat
dog
horse
sheep
cow
elephant
bear
zebra
giraffe
backpack
umbrella
handbag
tie
suitcase
frisbee
skis
snowboard
sports ball
kite
baseball bat
baseball glove
skateboard
surfboard
tennis racket
bottle
wine glass
cup
fork
knife
spoon
bowl
banana
apple
sandwich
orange
broccoli
carrot
hot dog
pizza
donut
cake
chair
couch
potted plant
bed
dining table
toilet
tv
laptop
mouse
remote
keyboard
cell phone
microwave
oven
toaster
sink
refrigerator
book
clock
vase
scissors
teddy bear
hair drier
toothbrush
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle_serving_client import Client
from paddle_serving_app.reader import *
import sys
import numpy as np
preprocess = Sequential([
File2Image(), BGR2RGB(),
Normalize([123.675, 116.28, 103.53], [58.395, 57.12, 57.375], False),
Resize((512, 512)), Transpose((2, 0, 1))
])
postprocess = RCNNPostprocess("label_list.txt", "output")
client = Client()
client.load_client_config("serving_client/serving_client_conf.prototxt")
client.connect(['127.0.0.1:9494'])
im = preprocess(sys.argv[1])
fetch_map = client.predict(
feed={
"image": im,
"scale_factor": np.array([1.0, 1.0]).reshape(-1),
},
fetch=["save_infer_model/scale_0.tmp_1"],
batch=False)
print(fetch_map)
# YOLOv3 model on Paddle Serving
([简体中文](./README_CN.md)|English)
### Get Model
```
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/2.0/yolov3_darknet53_270e_coco.tar
```
### Start the service
```
tar xf yolov3_darknet53_270e_coco.tar
python -m paddle_serving_server_gpu.serve --model serving_server --port 9494 --gpu_ids 0
```
### Perform prediction
```
python test_client.py 000000570688.jpg
```
# 使用Paddle Serving部署YOLOv3模型
(简体中文|[English](./README.md))
## 获得模型
```
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/2.0/yolov3_darknet53_270e_coco.tar
```
### 启动服务
```
tar xf yolov3_darknet53_270e_coco.tar
python -m paddle_serving_server_gpu.serve --model pddet_serving_model --port 9494 --gpu_ids 0
```
### 执行预测
```
python test_client.py 000000570688.jpg
```
person
bicycle
car
motorcycle
airplane
bus
train
truck
boat
traffic light
fire hydrant
stop sign
parking meter
bench
bird
cat
dog
horse
sheep
cow
elephant
bear
zebra
giraffe
backpack
umbrella
handbag
tie
suitcase
frisbee
skis
snowboard
sports ball
kite
baseball bat
baseball glove
skateboard
surfboard
tennis racket
bottle
wine glass
cup
fork
knife
spoon
bowl
banana
apple
sandwich
orange
broccoli
carrot
hot dog
pizza
donut
cake
chair
couch
potted plant
bed
dining table
toilet
tv
laptop
mouse
remote
keyboard
cell phone
microwave
oven
toaster
sink
refrigerator
book
clock
vase
scissors
teddy bear
hair drier
toothbrush
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle_serving_client import Client
from paddle_serving_app.reader import *
import sys
import numpy as np
preprocess = Sequential([
File2Image(), BGR2RGB(), Div(255.0),
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], False),
Resize((608, 608)), Transpose((2, 0, 1))
])
postprocess = RCNNPostprocess("label_list.txt", "output")
client = Client()
client.load_client_config("serving_client/serving_client_conf.prototxt")
client.connect(['127.0.0.1:9494'])
im = preprocess(sys.argv[1])
fetch_map = client.predict(
feed={
"image": im,
"im_shape": np.array(list(im.shape[1:])).reshape(-1),
"scale_factor": np.array([1.0, 1.0]).reshape(-1),
},
fetch=["save_infer_model/scale_0.tmp_1"],
batch=False)
print(fetch_map)
fetch_map["image"] = sys.argv[1]
postprocess(fetch_map)
......@@ -4,21 +4,20 @@
### Get The Faster RCNN Model
```
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/faster_rcnn_model.tar.gz
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/2.0/faster_rcnn.tar.gz
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/infer_cfg.yml
```
If you want to have more detection models, please refer to [Paddle Detection Model Zoo](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.2/docs/MODEL_ZOO_cn.md)
### Start the service
```
tar xf faster_rcnn_model.tar.gz
mv faster_rcnn_model/pddet* .
GLOG_v=2 python -m paddle_serving_server_gpu.serve --model pddet_serving_model --port 9494 --gpu_ids 0
tar xf faster_rcnn.tar.gz
GLOG_v=2 python -m paddle_serving_server_gpu.serve --model serving_server --port 9494 --gpu_ids 0
```
### Perform prediction
```
python test_client.py pddet_client_conf/serving_client_conf.prototxt infer_cfg.yml 000000570688.jpg
python test_client.py serving_client/serving_client_conf.prototxt infer_cfg.yml 000000570688.jpg
```
## 3. Result analysis
......
......@@ -23,7 +23,7 @@ preprocess = Sequential([
Resize(640, 640), Transpose((2, 0, 1))
])
postprocess = RCNNPostprocess("label_list.txt", "output")
postprocess = RCNNPostprocess("yolo_list.txt", "output")
client = Client()
client.load_client_config(sys.argv[1])
......@@ -36,7 +36,6 @@ fetch_map = client.predict(
"im_info": np.array(list(im.shape[1:]) + [1.0]),
"im_shape": np.array(list(im.shape[1:]) + [1.0])
},
fetch=["multiclass_nms"],
fetch=["save_infer_model/scale_2.tmp_1", "save_infer_model/scale_3.tmp_1"],
batch=False)
fetch_map["image"] = sys.argv[3]
postprocess(fetch_map)
print(fetch_map)
......@@ -32,7 +32,7 @@ if '${PACK}' == 'ON':
REQUIRED_PACKAGES = [
'six >= 1.10.0', 'sentencepiece', 'opencv-python', 'pillow',
'six >= 1.10.0', 'sentencepiece<=0.1.83', 'opencv-python<=4.2.0.32', 'pillow',
'pyclipper'
]
......
......@@ -44,7 +44,7 @@ if '${PACK}' == 'ON':
REQUIRED_PACKAGES = [
'six >= 1.10.0', 'protobuf >= 3.11.0', 'numpy >= 1.12', 'grpcio <= 1.33.2',
'grpcio-tools <= 1.33.2'
'grpcio-tools <= 1.33.2', 'requests'
]
......
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