mirrors
/
PaddleClas
mirror of https://github.com/PaddlePaddle/PaddleClas.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

Update HubServing (#870) 

* Update HubServing

* Fix the relative path

* Update doc of HubServing
pull/900/head
Tingquan Gao 2021-06-17 20:56:03 +08:00 committed by GitHub
parent d63e0e88cd
commit 666d912678
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
11 changed files with 1207 additions and 140 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

64
deploy/hubserving/clas/module.py
View File

@ -1,4 +1,4 @@
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
# Copyright (c) 2021 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.
@ -18,15 +18,14 @@ sys.path.insert(0, ".")
import time
from paddlehub.utils.log import logger
from paddlehub.module.module import moduleinfo, serving
import cv2
import numpy as np
import paddle.nn as nn
from paddlehub.module.module import moduleinfo, serving
from tools.infer.predict import Predictor
from tools.infer.utils import b64_to_np, postprocess
from deploy.hubserving.clas.params import read_params
from hubserving.clas.params import get_default_confg
from python.predict_cls import ClsPredictor
from utils import config
from utils.encode_decode import b64_to_np
@moduleinfo(
@ -41,19 +40,24 @@ class ClasSystem(nn.Layer):
"""
initialize with the necessary elements
"""
cfg = read_params()
self._config = self._load_config(
use_gpu=use_gpu, enable_mkldnn=enable_mkldnn)
self.cls_predictor = ClsPredictor(self._config)
def _load_config(self, use_gpu=None, enable_mkldnn=None):
cfg = get_default_confg()
cfg = config.AttrDict(cfg)
config.create_attr_dict(cfg)
if use_gpu is not None:
cfg.use_gpu = use_gpu
cfg.Global.use_gpu = use_gpu
if enable_mkldnn is not None:
cfg.enable_mkldnn = enable_mkldnn
cfg.hubserving = True
cfg.Global.enable_mkldnn = enable_mkldnn
cfg.enable_benchmark = False
self.args = cfg
if cfg.use_gpu:
if cfg.Global.use_gpu:
try:
_places = os.environ["CUDA_VISIBLE_DEVICES"]
int(_places[0])
print("Use GPU, GPU Memery:{}".format(cfg.gpu_mem))
print("Use GPU, GPU Memery:{}".format(cfg.Global.gpu_mem))
print("CUDA_VISIBLE_DEVICES: ", _places)
except:
raise RuntimeError(
@ -62,24 +66,36 @@ class ClasSystem(nn.Layer):
else:
print("Use CPU")
print("Enable MKL-DNN") if enable_mkldnn else None
self.predictor = Predictor(self.args)
return cfg
def predict(self, batch_input_data, top_k=1):
assert isinstance(
batch_input_data,
np.ndarray), "The input data is inconsistent with expectations."
def predict(self, inputs):
if not isinstance(inputs, list):
raise Exception(
"The input data is inconsistent with expectations.")
starttime = time.time()
batch_outputs = self.predictor.predict(batch_input_data)
outputs = self.cls_predictor.predict(inputs)
elapse = time.time() - starttime
batch_result_list = postprocess(batch_outputs, top_k)
return {"prediction": batch_result_list, "elapse": elapse}
preds = self.cls_predictor.postprocess(outputs)
return {"prediction": preds, "elapse": elapse}
@serving
def serving_method(self, images, revert_params, **kwargs):
def serving_method(self, images, revert_params):
"""
Run as a service.
"""
input_data = b64_to_np(images, revert_params)
results = self.predict(batch_input_data=input_data, **kwargs)
results = self.predict(inputs=list(input_data))
return results
if __name__ == "__main__":
import cv2
import paddlehub as hub
module = hub.Module(name="clas_system")
img_path = "./hubserving/ILSVRC2012_val_00006666.JPEG"
img = cv2.imread(img_path)[:, :, ::-1]
img = cv2.resize(img, (224, 224)).transpose((2, 0, 1))
res = module.predict([img.astype(np.float32)])
print("The returned result of {}: {}".format(img_path, res))

48
deploy/hubserving/clas/params.py
View File

@ -1,4 +1,4 @@
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
# Copyright (c) 2021 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.
@ -17,28 +17,24 @@ from __future__ import division
from __future__ import print_function
class Config(object):
pass
def read_params():
cfg = Config()
cfg.model_file = "./inference/cls_infer.pdmodel"
cfg.params_file = "./inference/cls_infer.pdiparams"
cfg.batch_size = 1
cfg.use_gpu = False
cfg.enable_mkldnn = False
cfg.ir_optim = True
cfg.gpu_mem = 8000
cfg.use_fp16 = False
cfg.use_tensorrt = False
cfg.cpu_num_threads = 10
cfg.enable_profile = False
# params for preprocess
cfg.resize_short = 256
cfg.resize = 224
cfg.normalize = True
return cfg
def get_default_confg():
return {
'Global': {
"inference_model_dir": "../inference/",
"batch_size": 1,
'use_gpu': False,
'use_fp16': False,
'enable_mkldnn': False,
'cpu_num_threads': 1,
'use_tensorrt': False,
'ir_optim': False,
"gpu_mem": 8000,
'enable_profile': False,
"enable_benchmark": False
},
'PostProcess': {
'name': 'Topk',
'topk': 5,
'class_id_map_file': './utils/imagenet1k_label_list.txt'
}
}

35
deploy/hubserving/clas/test.py
View File

@ -1,35 +0,0 @@
# 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 os
import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.abspath(os.path.join(__dir__, '../../../')))
import argparse
import numpy as np
import cv2
import paddlehub as hub
from tools.infer.utils import preprocess
args = argparse.Namespace(resize_short=256, resize=224, normalize=True)
img_path_list = ["./deploy/hubserving/ILSVRC2012_val_00006666.JPEG", ]
module = hub.Module(name="clas_system")
for i, img_path in enumerate(img_path_list):
img = cv2.imread(img_path)[:, :, ::-1]
img = preprocess(img, args)
batch_input_data = np.expand_dims(img, axis=0)
res = module.predict(batch_input_data)
print("The returned result of {}: {}".format(img_path, res))

64
deploy/hubserving/readme.md
View File

@ -4,7 +4,7 @@
hubserving服务部署配置服务包`clas`下包含3个必选文件，目录如下：
```
deploy/hubserving/clas/
hubserving/clas/
└─ __init__.py 空文件，必选
└─ config.json 配置文件，可选，使用配置启动服务时作为参数传入
└─ module.py 主模块，必选，包含服务的完整逻辑
@ -21,16 +21,16 @@ pip3 install paddlehub==2.0.0b1 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/s
### 2. 下载推理模型
安装服务模块前，需要准备推理模型并放到正确路径，默认模型路径为：
```
分类推理模型结构文件：./inference/cls_infer.pdmodel
分类推理模型权重文件：./inference/cls_infer.pdiparams
分类推理模型结构文件：PaddleClas/inference/inference.pdmodel
分类推理模型权重文件：PaddleClas/inference/inference.pdiparams
```
**注意**：
* 模型路径可在`./PaddleClas/deploy/hubserving/clas/params.py`中查看和修改。
* 模型文件路径可在`PaddleClas/deploy/hubserving/clas/params.py`中查看和修改：
```python
cfg.model_file = "./inference/cls_infer.pdmodel"
cfg.params_file = "./inference/cls_infer.pdiparams"
"inference_model_dir": "../inference/"
```
需要注意，模型文件（包括.pdmodel与.pdiparams）名称必须为`inference`。
* 我们也提供了大量基于ImageNet-1k数据集的预训练模型，模型列表及下载地址详见[模型库概览](../../docs/zh_CN/models/models_intro.md)，也可以使用自己训练转换好的模型。
### 3. 安装服务模块
@ -38,14 +38,17 @@ pip3 install paddlehub==2.0.0b1 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/s
* 在Linux环境下，安装示例如下：
```shell
# 安装服务模块：
hub install deploy/hubserving/clas/
cd PaddleClas/deploy
# 安装服务模块：
hub install hubserving/clas/
```
* 在Windows环境下(文件夹的分隔符为`\`)，安装示例如下：
```shell
cd PaddleClas\deploy
# 安装服务模块：
hub install deploy\hubserving\clas\
hub install hubserving\clas\
```
### 4. 启动服务
@ -59,7 +62,6 @@ $ hub serving start --modules Module1==Version1 \
```
**参数：**
|参数|用途|
|-|-|
|--modules/-m| [**必选**] PaddleHub Serving预安装模型，以多个Module==Version键值对的形式列出<br>*`当不指定Version时，默认选择最新版本`*|
@ -108,30 +110,32 @@ $ hub serving start --modules Module1==Version1 \
如，使用GPU 3号卡启动串联服务：
```shell
cd PaddleClas/deploy
export CUDA_VISIBLE_DEVICES=3
hub serving start -c deploy/hubserving/clas/config.json
hub serving start -c hubserving/clas/config.json
```
## 发送预测请求
配置好服务端，可使用以下命令发送预测请求，获取预测结果:
```python tools/test_hubserving.py server_url image_path```
```shell
cd PaddleClas/deploy
python hubserving/test_hubserving.py server_url image_path
```
需要给脚本传递2个必须参数：
- **server_url**：服务地址，格式为
`http://[ip_address]:[port]/predict/[module_name]`
- **image_path**：测试图像路径，可以是单张图片路径，也可以是图像集合目录路径。
- **top_k**：[**可选**] 返回前 `top_k` 个 `score` ，默认为 `1`。
- **batch_size**：[**可选**] 以`batch_size`大小为单位进行预测，默认为`1`。
- **resize_short**：[**可选**] 将图像等比例缩放到最短边为`resize_short`，默认为`256`。
- **resize**：[**可选**] 将图像resize到`resize * resize`尺寸，默认为`224`。
- **normalize**：[**可选**] 是否对图像进行normalize处理，默认为`True`。
**注意**：如果使用`Transformer`系列模型，如`DeiT_***_384`, `ViT_***_384`等，请注意模型的输入数据尺寸。需要指定`--resize_short=384 --resize=384`。
访问示例：
```python tools/test_hubserving.py --server_url http://127.0.0.1:8866/predict/clas_system --image_file ./deploy/hubserving/ILSVRC2012_val_00006666.JPEG --top_k 5```
```shell
python hubserving/test_hubserving.py --server_url http://127.0.0.1:8866/predict/clas_system --image_file ./hubserving/ILSVRC2012_val_00006666.JPEG --batch_size 8
```
### 返回结果格式说明
返回结果为列表（list），包含top-k个分类结果，以及对应的得分，还有此图片预测耗时，具体如下：
@ -143,7 +147,7 @@ list: 返回结果
└─ float: 该图分类耗时，单位秒
```
**说明：** 如果需要增加、删除、修改返回字段，可在相应模块的`module.py`文件中进行修改，完整流程参考下一节自定义修改服务模块。
**说明：** 如果需要增加、删除、修改返回字段，可对相应模块进行修改，完整流程参考下一节自定义修改服务模块。
## 自定义修改服务模块
如果需要修改服务逻辑，你一般需要操作以下步骤：
@ -151,16 +155,30 @@ list: 返回结果
- 1、 停止服务
```hub serving stop --port/-p XXXX```
- 2、 到相应的`module.py`和`params.py`等文件中根据实际需求修改代码。
例如，例如需要替换部署服务所用模型，则需要到`params.py`中修改模型路径参数`cfg.model_file`和`cfg.params_file`。
修改并安装（`hub install deploy/hubserving/clas/`）完成后，在进行部署前，可通过`python deploy/hubserving/clas/test.py`测试已安装服务模块。
- 2、 到相应的`module.py`和`params.py`等文件中根据实际需求修改代码。`module.py`修改后需要重新安装（`hub install hubserving/clas/`）并部署。在进行部署前，可通过`python hubserving/clas/module.py`测试已安装服务模块。
- 3、 卸载旧服务包
```hub uninstall clas_system```
- 4、 安装修改后的新服务包
```hub install deploy/hubserving/clas/```
```hub install hubserving/clas/```
- 5、重新启动服务
```hub serving start -m clas_system```
**注意**：
常用参数可在[params.py](./clas/params.py)中修改：
* 更换模型，需要修改模型文件路径参数:
```python
"inference_model_dir":
```
* 更改后处理时返回的`top-k`结果数量：
```python
'topk':
```
* 更改后处理时的lable与class id对应映射文件：
```python
'class_id_map_file':
```
为了避免不必要的延时以及能够以batch_size进行预测，数据预处理逻辑（包括resize、crop等操作）在客户端完成，因此需要在[test_hubserving.py](./test_hubserving.py#L35-L52)中修改。

82
deploy/hubserving/test_hubserving.py
View File

@ -1,4 +1,4 @@
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
# Copyright (c) 2021 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.
@ -15,29 +15,54 @@
import os
import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
sys.path.append(os.path.abspath(os.path.join(__dir__, '../')))
from tools.infer.utils import parse_args, get_image_list, preprocess, np_to_b64
from ppcls.utils import logger
import numpy as np
import cv2
import time
import requests
import json
import base64
import argparse
import numpy as np
import cv2
from utils import logger
from utils.get_image_list import get_image_list
from utils import config
from utils.encode_decode import np_to_b64
from python.preprocess import create_operators
preprocess_config = [{
'ResizeImage': {
'resize_short': 256
}
}, {
'CropImage': {
'size': 224
}
}, {
'NormalizeImage': {
'scale': 0.00392157,
'mean': [0.485, 0.456, 0.406],
'std': [0.229, 0.224, 0.225],
'order': ''
}
}, {
'ToCHWImage': None
}]
def main(args):
image_path_list = get_image_list(args.image_file)
headers = {"Content-type": "application/json"}
preprocess_ops = create_operators(preprocess_config)
cnt = 0
predict_time = 0
all_score = 0.0
start_time = time.time()
batch_input_list = []
img_data_list = []
img_name_list = []
cnt = 0
for idx, img_path in enumerate(image_path_list):
@ -48,22 +73,23 @@ def main(args):
format(img_path))
continue
else:
img = img[:, :, ::-1]
data = preprocess(img, args)
batch_input_list.append(data)
for ops in preprocess_ops:
img = ops(img)
img = np.array(img)
img_data_list.append(img)
img_name = img_path.split('/')[-1]
img_name_list.append(img_name)
cnt += 1
if cnt % args.batch_size == 0 or (idx + 1) == len(image_path_list):
batch_input = np.array(batch_input_list)
b64str, revert_shape = np_to_b64(batch_input)
inputs = np.array(img_data_list)
b64str, revert_shape = np_to_b64(inputs)
data = {
"images": b64str,
"revert_params": {
"shape": revert_shape,
"dtype": str(batch_input.dtype)
},
"top_k": args.top_k
"dtype": str(inputs.dtype)
}
}
try:
r = requests.post(
@ -80,24 +106,25 @@ def main(args):
continue
else:
results = r.json()["results"]
batch_result_list = results["prediction"]
preds = results["prediction"]
elapse = results["elapse"]
cnt += len(batch_result_list)
cnt += len(preds)
predict_time += elapse
for number, result_list in enumerate(batch_result_list):
for number, result_list in enumerate(preds):
all_score += result_list["scores"][0]
result_str = ""
for i in range(len(result_list["clas_ids"])):
for i in range(len(result_list["class_ids"])):
result_str += "{}: {:.2f}\t".format(
result_list["clas_ids"][i],
result_list["class_ids"][i],
result_list["scores"][i])
logger.info("File:{}, The top-{} result(s): {}".format(
img_name_list[number], args.top_k, result_str))
logger.info("File:{}, The result(s): {}".format(
img_name_list[number], result_str))
finally:
batch_input_list = []
img_data_list = []
img_name_list = []
total_time = time.time() - start_time
@ -109,5 +136,10 @@ def main(args):
if __name__ == '__main__':
args = parse_args()
parser = argparse.ArgumentParser()
parser.add_argument("--server_url", type=str)
parser.add_argument("--image_file", type=str)
parser.add_argument("--batch_size", type=int, default=1)
args = parser.parse_args()
main(args)

16
deploy/python/predict_cls.py
View File

@ -24,16 +24,22 @@ from utils import logger
from utils import config
from utils.predictor import Predictor
from utils.get_image_list import get_image_list
from preprocess import create_operators
from postprocess import build_postprocess
from python.preprocess import create_operators
from python.postprocess import build_postprocess
class ClsPredictor(Predictor):
def __init__(self, config):
super().__init__(config["Global"])
self.preprocess_ops = create_operators(config["PreProcess"][
"transform_ops"])
self.postprocess = build_postprocess(config["PostProcess"])
self.preprocess_ops = []
self.postprocess = None
if "PreProcess" in config:
if "transform_ops" in config["PreProcess"]:
self.preprocess_ops = create_operators(config["PreProcess"][
"transform_ops"])
if "PostProcess" in config:
self.postprocess = build_postprocess(config["PostProcess"])
def predict(self, images):
input_names = self.paddle_predictor.get_input_names()

2
deploy/python/preprocess.py
View File

@ -26,7 +26,7 @@ import cv2
import numpy as np
import importlib
from det_preprocess import DetNormalizeImage, DetPadStride, DetPermute, DetResize
from python.det_preprocess import DetNormalizeImage, DetPadStride, DetPermute, DetResize
def create_operators(params):

1
deploy/utils/__init__.py
View File

@ -2,3 +2,4 @@ from . import logger
from . import config
from . import get_image_list
from . import predictor
from . import encode_decode

4
deploy/utils/config.py
View File

@ -1,4 +1,4 @@
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -16,7 +16,9 @@ import os
import copy
import argparse
import yaml
from utils import logger
__all__ = ['get_config']

31
deploy/utils/encode_decode.py 100644
View File

@ -0,0 +1,31 @@
# Copyright (c) 2021 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 base64
import numpy as np
def np_to_b64(images):
img_str = base64.b64encode(images).decode('utf8')
return img_str, images.shape
def b64_to_np(b64str, revert_params):
shape = revert_params["shape"]
dtype = revert_params["dtype"]
dtype = getattr(np, dtype) if isinstance(str, type(dtype)) else dtype
data = base64.b64decode(b64str.encode('utf8'))
data = np.fromstring(data, dtype).reshape(shape)
return data

1000
deploy/utils/imagenet1k_label_list.txt 100644
View File

File diff suppressed because it is too large Load Diff